WO2021114809A1 - Procédé et appareil de détection d'endommagement de véhicule, dispositif informatique et support de stockage - Google Patents

Procédé et appareil de détection d'endommagement de véhicule, dispositif informatique et support de stockage Download PDF

Info

Publication number
WO2021114809A1
WO2021114809A1 PCT/CN2020/116741 CN2020116741W WO2021114809A1 WO 2021114809 A1 WO2021114809 A1 WO 2021114809A1 CN 2020116741 W CN2020116741 W CN 2020116741W WO 2021114809 A1 WO2021114809 A1 WO 2021114809A1
Authority
WO
WIPO (PCT)
Prior art keywords
feature
adaptive
damage
model
vehicle
Prior art date
Application number
PCT/CN2020/116741
Other languages
English (en)
Chinese (zh)
Inventor
康甲
刘莉红
刘玉宇
肖京
Original Assignee
平安科技（深圳）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 平安科技（深圳）有限公司 filed Critical 平安科技（深圳）有限公司
Publication of WO2021114809A1 publication Critical patent/WO2021114809A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/048—Activation functions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/084—Backpropagation, e.g. using gradient descent
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour

Definitions

  • This application relates to the field of artificial intelligence image classification, and in particular to a vehicle damage feature detection method, device, computer equipment and storage medium.
  • insurance companies generally manually identify the images taken by the owner or business personnel of the vehicle damage after the traffic accident , That is, to manually identify and determine the damage type and damaged area of the damaged part of the vehicle in the image.
  • the artificially recognized damage type and damaged area may not match; for example: Because it is difficult to distinguish between dents and scratches through visual images, damage assessment personnel can easily determine the type of damage caused by the dent as the type of scratch damage.
  • the miscalculation caused by the above conditions will greatly reduce the accuracy of the damage assessment; While it may cause cost losses for the insurance company, it will also reduce the satisfaction of car owners or customers; in addition, the manual loss determination workload is huge and the loss determination efficiency is low. When a certain loss determination accuracy needs to be met, Will further increase the workload and reduce work efficiency.
  • the present application provides a vehicle damage feature detection method, device, computer equipment, and storage medium, which realize the rapid and accurate automatic identification of the damage type and damage area corresponding to the damaged part of the vehicle in the damage image of the vehicle to be detected, which greatly reduces
  • the process of model construction and model training have improved the accuracy and reliability of determining the type of loss and the area of the loss, and improved the efficiency of the loss.
  • a vehicle damage feature detection method including:
  • the damage image of the vehicle to be detected After receiving the vehicle damage detection instruction, obtain the damage image of the vehicle to be detected; the damage image of the vehicle to be detected includes at least one image of the damaged location of the vehicle;
  • the vehicle damage image to be detected is input into an unsupervised domain adaptive network model;
  • the unsupervised domain adaptive network model includes a pytorch-based migration learning model, a strong local feature adaptive model, a weak global feature adaptive model, and regularization model;
  • the pytorch-based migration learning model outputs a migration feature vector group according to the vehicle characteristics, and at the same time, a first adaptive feature vector group is acquired through the strong local feature adaptive model, and acquired through the weak global feature adaptive model
  • the second adaptive feature vector group the first adaptive feature vector group is the strong local feature adaptive model obtained and output according to the first vehicle damage feature extracted from the local feature map
  • the second self The adaptive feature vector group is obtained by the weak global feature adaptive model according to the second vehicle damage feature extracted from the global feature map and output;
  • the migration feature vector group, the first adaptive feature vector group, and the second adaptive feature vector group are input into the regularization model, and the migration feature vector group, the The first adaptive feature vector group and the second adaptive feature vector group are subjected to regularization processing to obtain a recognition result that includes the damage type and the damage area; the recognition result represents that the damage image of the vehicle to be detected contains all the damaged images.
  • the type of damage and the result of the corresponding damage area are input into the regularization model, and the migration feature vector group, the The first adaptive feature vector group and the second adaptive feature vector group are subjected to regularization processing to obtain a recognition result that includes the damage type and the damage area; the recognition result represents that the damage image of the vehicle to be detected contains all the damaged images.
  • the type of damage and the result of the corresponding damage area is a recognition result that includes the damage type and the damage area.
  • a vehicle damage feature detection device including:
  • the receiving module is configured to obtain the damage image of the vehicle to be detected after receiving the vehicle damage detection instruction; the damage image of the vehicle to be detected includes at least one image of the damaged location of the vehicle;
  • the input module is used to input the to-be-detected vehicle damage image into an unsupervised domain adaptive network model;
  • the unsupervised domain adaptive network model includes a pytorch-based migration learning model, a strong local feature adaptive model, and a weak global feature automatic Adaptation model and regularization model;
  • An extraction module for extracting the vehicle features of the damage image of the vehicle to be detected through the pytorch-based migration learning model and generating a local feature map and a global feature map; the vehicle features are features related to the vehicle after the migration learning;
  • the output module is configured to output a transfer feature vector set according to the vehicle characteristics through the pytorch-based transfer learning model, and at the same time obtain a first adaptive feature vector set through the strong local feature adaptive model, and use the weak global
  • the feature adaptive model acquires a second adaptive feature vector group; the first adaptive feature vector group is the strong local feature adaptive model acquired and output according to the first vehicle damage feature extracted from the local feature map;
  • the second adaptive feature vector group is obtained and output by the weak global feature adaptive model according to the second vehicle damage feature extracted from the global feature map;
  • the recognition module is configured to input the migration feature vector group, the first adaptive feature vector group, and the second adaptive feature vector group into the regularization model, and use the regularization model to compare the migration feature
  • the vector group, the first adaptive feature vector group, and the second adaptive feature vector group are subjected to regularization processing to obtain a recognition result including the damage type and the damage area; the recognition result represents the damage of the vehicle to be detected
  • the image contains the results of all damaged types and corresponding damaged areas.
  • a computer device includes a memory, a processor, and computer-readable instructions that are stored in the memory and can run on the processor, and the processor implements the following steps when the processor executes the computer-readable instructions:
  • the damage image of the vehicle to be detected After receiving the vehicle damage detection instruction, obtain the damage image of the vehicle to be detected; the damage image of the vehicle to be detected includes at least one image of the damaged location of the vehicle;
  • the vehicle damage image to be detected is input into an unsupervised domain adaptive network model;
  • the unsupervised domain adaptive network model includes a pytorch-based migration learning model, a strong local feature adaptive model, a weak global feature adaptive model, and regularization model;
  • the pytorch-based migration learning model outputs a migration feature vector group according to the vehicle characteristics, and at the same time, a first adaptive feature vector group is acquired through the strong local feature adaptive model, and acquired through the weak global feature adaptive model
  • the second adaptive feature vector group the first adaptive feature vector group is the strong local feature adaptive model obtained and output according to the first vehicle damage feature extracted from the local feature map
  • the second self The adaptive feature vector group is obtained by the weak global feature adaptive model according to the second vehicle damage feature extracted from the global feature map and output;
  • the migration feature vector group, the first adaptive feature vector group, and the second adaptive feature vector group are input into the regularization model, and the migration feature vector group, the The first adaptive feature vector group and the second adaptive feature vector group are subjected to regularization processing to obtain a recognition result that includes the damage type and the damage area; the recognition result represents that the damage image of the vehicle to be detected contains all the damaged images.
  • the type of damage and the result of the corresponding damage area are input into the regularization model, and the migration feature vector group, the The first adaptive feature vector group and the second adaptive feature vector group are subjected to regularization processing to obtain a recognition result that includes the damage type and the damage area; the recognition result represents that the damage image of the vehicle to be detected contains all the damaged images.
  • the type of damage and the result of the corresponding damage area is a recognition result that includes the damage type and the damage area.
  • One or more readable storage media storing computer readable instructions, when the computer readable instructions are executed by one or more processors, the one or more processors execute the following steps:
  • the damage image of the vehicle to be detected After receiving the vehicle damage detection instruction, obtain the damage image of the vehicle to be detected; the damage image of the vehicle to be detected includes at least one image of the damaged location of the vehicle;
  • the vehicle damage image to be detected is input into an unsupervised domain adaptive network model;
  • the unsupervised domain adaptive network model includes a pytorch-based migration learning model, a strong local feature adaptive model, a weak global feature adaptive model, and regularization model;
  • the pytorch-based migration learning model outputs a migration feature vector group according to the vehicle characteristics, and at the same time, a first adaptive feature vector group is acquired through the strong local feature adaptive model, and acquired through the weak global feature adaptive model
  • the second adaptive feature vector group the first adaptive feature vector group is the strong local feature adaptive model obtained and output according to the first vehicle damage feature extracted from the local feature map
  • the second self The adaptive feature vector group is obtained by the weak global feature adaptive model according to the second vehicle damage feature extracted from the global feature map and output;
  • the migration feature vector group, the first adaptive feature vector group, and the second adaptive feature vector group are input into the regularization model, and the migration feature vector group, the The first adaptive feature vector group and the second adaptive feature vector group are subjected to regularization processing to obtain a recognition result that includes the damage type and the damage area; the recognition result represents that the damage image of the vehicle to be detected contains all the damaged images.
  • the type of damage and the result of the corresponding damage area are input into the regularization model, and the migration feature vector group, the The first adaptive feature vector group and the second adaptive feature vector group are subjected to regularization processing to obtain a recognition result that includes the damage type and the damage area; the recognition result represents that the damage image of the vehicle to be detected contains all the damaged images.
  • the type of damage and the result of the corresponding damage area is a recognition result that includes the damage type and the damage area.
  • the vehicle damage feature detection method, device, computer equipment, and storage medium provided in the present application obtain the damage image of the vehicle to be detected; input the damage image of the vehicle to be detected into the unsupervised domain adaptive network model; the unsupervised domain is adaptive
  • the network model includes a pytorch-based migration learning model, a strong local feature adaptive model, a weak global feature adaptive model, and a regularization model; the pytorch-based migration learning model extracts the vehicle features of the damaged image of the vehicle to be detected, and all
  • the pytorch-based transfer learning model generates a local feature map and a global feature map in the process of extracting the vehicle feature; the vehicle feature is the feature related to the vehicle after the transfer learning; the transfer learning model based on the pytorch Output the transfer feature vector group according to the vehicle characteristics, and at the same time obtain the first adaptive feature vector group through the strong local feature adaptive model, and acquire the second adaptive feature vector group through the weak global feature adaptive model;
  • An adaptive feature vector group and the second adaptive feature vector group are subjected to regularization processing to obtain a recognition result including the damage type and the damage area.
  • the transfer learning pytorch model and the strong local feature adaptive model are strengthened.
  • the second vehicle damage feature framework is an unsupervised domain adaptive network model suitable for vehicle damage detection, which can quickly and accurately automatically identify the vehicle damage in the vehicle damage image to be detected.
  • the damage type and damage area corresponding to the position of greatly reduce the process of model construction and model training, improve the accuracy and reliability of determining the type of damage and the area of damage, and improve the efficiency of damage.
  • FIG. 1 is a schematic diagram of an application environment of a method for detecting a vehicle damage feature in an embodiment of the present application
  • FIG. 2 is a flowchart of a method for detecting damage characteristics of a vehicle in an embodiment of the present application
  • step S20 is a flowchart of step S20 of the method for detecting damage features of a vehicle in an embodiment of the present application
  • step S20 is a flowchart of step S20 of a method for detecting damage characteristics of a vehicle in another embodiment of the present application
  • step S40 of the method for detecting damage features of a vehicle in an embodiment of the present application
  • step S40 of the method for detecting damage features of a vehicle in another embodiment of the present application
  • Fig. 7 is a schematic block diagram of a vehicle damage feature detection device in an embodiment of the present application.
  • FIG. 8 is a functional block diagram of the output module 14 of the vehicle damage feature detection device in an embodiment of the present application.
  • Fig. 9 is a schematic diagram of a computer device in an embodiment of the present application.
  • the vehicle damage feature detection method provided by the present application can be applied in the application environment as shown in Fig. 1, in which the client (computer equipment) communicates with the server through the network.
  • the client includes, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, cameras, and portable wearable devices.
  • the server can be implemented as an independent server or a server cluster composed of multiple servers.
  • a vehicle damage feature detection method is provided, and the technical solution mainly includes the following steps S10-S50:
  • the damage image of the vehicle to be detected includes at least one image of the damaged location of the vehicle.
  • the vehicle damage detection instruction is triggered to obtain the vehicle damage image to be detected, and the vehicle damage image to be detected contains at least one damage to the vehicle.
  • the image of the location, the damage includes 7 damage conditions such as scratches, scratches, dents, wrinkles, dead folds, tears, and missing.
  • the acquisition method can be set according to requirements, for example, through the vehicle damage detection instruction
  • the damage image of the vehicle to be detected contained in it is directly acquired, or the vehicle damage detection instruction contains a storage path for storing the damage image of the vehicle to be detected, and then it is obtained through the accessed storage path, and so on.
  • the unsupervised domain adaptive network model includes a pytorch-based migration learning model, a strong local feature adaptive model, a weak global feature adaptive model, and Regularization model.
  • the unsupervised domain adaptive network model is a trained adaptive network model
  • the unsupervised domain adaptive network model includes a pytorch-based transfer learning model, a strong local feature adaptive model, and a weak global feature self-adaptive model.
  • Adaptation model and regularization model, the pytorch-based migration learning model is a neural network model that transfers a pytorch-based network structure and is trained (that is, the trained pytorch model), and the characteristics of the trained pytorch model The extraction can be selected according to requirements.
  • the trained pytorch model is a pytorch model applied to vehicle lamp brightness detection, or the trained pytorch model is a pytorch model applied to vehicle model detection, etc.
  • the local feature adaptive model is a trained neural network model used to strengthen the first vehicle damage feature
  • the weak global feature adaptive model is a trained neural network model used to extract the second vehicle damage feature
  • the regular The transformation model is a model that normalizes the received feature vector.
  • the method before step S20, that is, before inputting the damage image of the vehicle to be detected into an unsupervised domain adaptive network model, the method includes:
  • the car damage sample set includes car damage sample images, one of the car damage sample images is associated with a damage label group; the damage label group includes at least one damage label type and at least one damage label area.
  • the car damage sample set includes a plurality of the car damage sample images
  • the car damage sample set is a set of the car damage samples
  • the car damage sample images are historically captured and contain at least one An image of the damaged location of a vehicle
  • one of the damaged sample images is associated with a damage label group
  • the damage label group includes damage label types and damage label areas
  • the damage label types include scratches, scratches, dents, and wrinkles
  • the damage label area is a coordinate area that can cover the damage location through a rectangular frame with a minimum area.
  • the car damage sample image is input into the adaptive network model
  • the adaptive network model is a deep convolutional neural network model including the initial parameters
  • the initial parameters can be performed according to requirements Setting, for example, the initial parameter is a randomly preset parameter, or a preset fixed value, etc.
  • the initial parameter obtains all the parameters in the trained deep convolutional neural network model through migration learning.
  • step S202 that is, before inputting the car damage sample image into an adaptive network model containing initial parameters, the method includes:
  • S20201 Obtain all the migration parameters of the trained pytorch model through migration learning, and determine all the migration parameters as the initial parameters in the adaptive network model.
  • the transfer learning is to transfer the model parameters that have been trained to a new model to help the new model training. Since most of the data or tasks are related, we can use transfer learning to transfer The learned model parameters are shared with the new model in a certain way to speed up and optimize the learning efficiency of the model. There is no need to start training and learning from scratch. In this way, the vehicle-related detection model can be optimized and accelerated through migration learning.
  • the trained pytorch model selects a vehicle-related detection model according to requirements, for example: the trained pytorch model is a pytorch model applied to vehicle lamp brightness detection, or the trained pytorch model is applied to vehicle model detection
  • the pytorch model of the pytorch model, etc., the pytorch model contains the migration parameters, the migration parameters are the parameters of the pytorch model, and all the migration parameters are determined as the initial parameters in the adaptive network model
  • the characteristic of the pytorch model is the calculation of dynamic graphs and a simple model structure (that is, the progression from the data tensor to the abstraction level of the network), which can improve the extraction efficiency and recognition accuracy of the model.
  • the pytorch model completed through migration learning training in this application can quickly build the model, reduce the time for training the pytorch model, and reduce the cost.
  • S203 Perform training feature extraction on the car damage sample image through the adaptive network model, and obtain a training result corresponding to the car damage sample image output by the adaptive network model according to the training feature; the training feature Including the vehicle feature, the first vehicle damage feature, and the second vehicle damage feature; the training result includes at least one sample damage type and at least one sample damage area.
  • the training feature includes the vehicle feature, the first vehicle damage feature, and the second vehicle damage feature
  • the vehicle feature is a feature related to the vehicle after migration learning
  • the damage feature is the feature of the local texture and color depth in the image
  • the second vehicle damage feature is the feature of the common vector characteristics in all feature maps
  • the training result includes the sample damage type and the sample damage area.
  • One of the sample damage An area corresponds to one damage type of the sample, and one damage type of the sample can correspond to multiple damaged areas of the sample.
  • the sample damage types include scratches, scratches, dents, folds, dead folds, tears, missing, etc. 7
  • the sample damage area is a damaged area range in the car damage sample image, that is, a rectangular coordinate range for identifying the damage position in the car damage sample image.
  • S204 Input all the damage label types, all the damage label regions, all the sample damage types, and all the sample damage regions corresponding to the car damage sample image into the loss model in the adaptive network model, And calculate the loss value through the loss function of the loss model.
  • the loss value corresponding to the car damage sample image is calculated, the loss function can be set according to requirements, and the loss function is between all the damage label types and all the sample damage types
  • the weighting function of the logarithm of the difference and the logarithm of the difference between all the damage label areas and all the sample damage areas, the loss value can be calculated through the loss function, and the loss value measures all The index of the sum of the gap between the damage label type and all the sample damage types and the gap between all the damage label areas and all the sample damage areas.
  • the convergence condition may be a condition that the loss value is less than a set threshold, that is, when the loss value is less than a set threshold, the adaptive network model after convergence is recorded as unsupervised domain adaptation The network model, and the unsupervised domain adaptive network model is stored in the blockchain.
  • the method further includes:
  • the convergence condition may also be a condition that the value of the loss value is small and will not drop after 10,000 calculations, that is, the value of the loss value is small and does not decrease after 10,000 calculations.
  • stop training record the self-adaptive network model after convergence as an unsupervised domain-adaptive network model, and store the unsupervised domain-adaptive network model in the blockchain.
  • the aforementioned unsupervised domain adaptive network model can also be stored in the nodes of the blockchain.
  • Blockchain essentially a decentralized database
  • Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.
  • the decentralized and fully distributed DNS service provided by the blockchain can realize the query and resolution of domain names through the point-to-point data transmission service between various nodes in the network, which can be used to ensure that the operating system and firmware of an important infrastructure are not available.
  • the initial parameters of the iterative adaptive network model are continuously updated, which can continuously move closer to the accurate recognition result, and make the accuracy of the recognition result higher and higher.
  • This application obtains a car damage sample set; the car damage sample set includes car damage sample images, and one car damage sample image is associated with a damage label group; the damage label group includes at least one damage label type and at least one damage Label area; input the car damage sample image into an adaptive network model containing initial parameters; perform training feature extraction on the car damage sample image through the adaptive network model, and obtain the adaptive network model according to the training
  • the training feature includes the vehicle feature, the first vehicle damage feature, and the second vehicle damage feature;
  • the training result includes at least one sample damage type And at least one sample damage area; input all the damage label types, all the damage label areas, all the sample damage types, and all the sample damage areas corresponding to the car damage sample image into the adaptive network model And calculate the loss value through the loss function of the loss model; when the loss value reaches the preset convergence condition, record the self-adaptive network model after convergence as an unsupervised domain self-adaptive network Model; when the loss value does not reach the preset
  • the vehicle damage sample image containing the damage label type and the damage label area is input into the adaptive network model, and the vehicle damage sample image is trained through the adaptive network model.
  • Features including the vehicle characteristics, the first vehicle The damage feature and the second vehicle damage feature
  • the training result output by the adaptive network model is obtained, and all the damage label types and all the damage label types corresponding to the car damage sample image are obtained through the loss function in the loss model.
  • the damage label area, all the sample damage types, and the loss value determined by all the sample damage areas are trained according to the loss value, and the adaptive network model after convergence is determined as an unsupervised domain adaptive network model, which provides A model training method for quickly identifying the damage in the car damage sample image, which improves the accuracy and reliability of determining the type of damage and the area of the damage, improves the efficiency of the damage, and shortens the training time. cost.
  • the local feature map and the global feature map are generated, and the local feature map is
  • a feature map output by a convolutional layer is taken as the local feature map.
  • a feature map output by a convolutional layer in the middle is selected as the local feature map. The feature map is because the convolutional layer in the middle range contains the feature vector of the first vehicle damage feature, that is, the local feature vector is extracted from the entire image.
  • the global feature map is the input of the pytorch-based migration
  • the feature map of the RPN model in the learning model is a feature map that convolves the to-be-detected vehicle damage image to a preset minimum size, which is convenient for extracting the second vehicle damage feature.
  • S40 Output a set of transfer feature vectors according to the vehicle characteristics through the pytorch-based transfer learning model, and at the same time obtain a first set of adaptive feature vectors through the strong local feature adaptive model, and adapt through the weak global feature
  • the model acquires a second adaptive feature vector group; the first adaptive feature vector group is the strong local feature adaptive model acquiring and outputting according to the first vehicle damage feature extracted from the local feature map; the first The second adaptive feature vector group is the weak global feature adaptive model obtained and output according to the second vehicle damage feature extracted from the global feature map.
  • the first vehicle damage feature is a feature of local texture and color depth in the image
  • the second vehicle damage feature is a feature of a common vector characteristic in all feature maps
  • the strong local feature adaptive model The function is to enhance the damage feature in the local feature map, generate useful adaptation information by extracting the first vehicle damage feature, and output the enhanced first feature vector, that is, extract the first adaptive feature vector Group
  • the function of the weak global feature adaptive model is to extract the second vehicle damage feature from the weaker feature vectors in all the global feature maps, prevent overfitting, and generate a second feature vector, namely The second adaptive feature vector group is extracted.
  • the strong local feature adaptive model extracts the first vehicle damage feature from the local feature map, and the strong local feature adaptive model Outputting a first adaptive feature vector group according to the first vehicle damage feature includes:
  • the local convolutional layer includes a first convolutional layer, a second convolutional layer, and a third convolutional layer.
  • the first convolutional layer includes a 3 ⁇ 3 ⁇ 512 convolution kernel and The first convolution with a step size of 2, a first filling layer filled with 1, a first batch normalization module (batch normalization), a first activation module (ReLU) and a first dropout module (dropout), so
  • the second convolutional layer includes a 3 ⁇ 3 ⁇ 128 convolution kernel and a second convolution with a step size of 2, a second filling layer filled with 1, a second batch of normalization modules (batch normalization), and a first Two activation modules and a second dropout module.
  • the third convolutional layer includes a 3 ⁇ 3 ⁇ 128 convolution kernel and a third convolution with a step size of 2, and a third filling layer filled with 1 ,
  • a third batch of normalization module (batch normalization), a third activation module and a third dropout module (dropout).
  • the first vehicle damage feature is a local texture and color depth feature in the image
  • the first vehicle damage feature extraction is performed on the local feature map through the first convolutional layer
  • the local The feature map is processed to reduce the dimensionality of the feature map and expand the number of channels of the feature map to obtain a first convolutional feature map, input the first convolutional feature map to the second convolutional layer, and pass the second convolution
  • the layer performs the first vehicle damage feature extraction on the first convolution feature map, and performs processing on the first convolution feature map to reduce the dimension of the feature map and expand the number of channels of the feature map to obtain the second convolution
  • the second convolution feature map is input to the third convolution layer
  • the first vehicle damage feature extraction is performed on the second convolution feature map through the third convolution layer
  • the second convolution feature map is processed to reduce the dimension of the feature map and expand the number of channels of the feature map to obtain the local feature vector map.
  • the dimension of the local feature vector map is less than the dimension of the local feature map, so The
  • S402 Input the local feature vector graph to a pooling layer in the strong local feature adaptive model, and perform pooling processing on the local feature vector graph through the pooling layer to obtain a local pooling matrix.
  • the method of pooling treatment can be set according to requirements.
  • the pooling treatment can be average pooling or maximum pooling, etc.
  • the function of the pooling treatment is to affect the local characteristics.
  • Vector graph dimensionality reduction processing, the local pooling matrix is a one-dimensional matrix array.
  • S403 Input the local pooling matrix into a fully connected layer in the strong local feature adaptive model, and perform feature connections on the local pooling matrix through the fully connected layer to obtain a local connected matrix.
  • the feature connection is to map the obtained feature vector value to the position of the sample label space, and perform weighted summation, connect these feature vectors, and feature the local pooling matrix through the fully connected layer Connect to obtain the local connection matrix, and the local connection matrix is a sorted one-dimensional matrix array. For example: convolve through 300 1 ⁇ 1 convolution kernels and connect them into a one-dimensional group of 300 vectors.
  • the regression processing is to perform a normalization operation after weighting the input to obtain the score of each category, and then through the process of softmax mapping to probability, the Softmax layer predicts and classifies the local connection matrix Obtain the first adaptive feature vector group.
  • the local feature map is input into the local convolutional layer, and the first vehicle damage feature in the local feature map is extracted through the local convolution layer to obtain a local feature vector map;
  • the feature vector graph is input to the pooling layer, and the local feature vector graph is pooled by the pooling layer to obtain a local pooling matrix;
  • the local pooling matrix is input to the fully connected layer, and the local pooling matrix is input to the fully connected layer.
  • the fully connected layer performs feature connection on the local pooling matrix to obtain a local connection matrix; input the local connection matrix to the Softmax layer, and perform regression processing on the local connection matrix through the Softmax layer to obtain the The first adaptive feature vector group corresponding to the local feature map.
  • the weak global feature adaptive model extracts the second vehicle damage feature from the global feature map, and the global feature adaptive model is based on
  • the second vehicle damage feature outputting a second adaptive feature vector group includes:
  • the global convolutional layer includes a first global convolutional layer, a second global convolutional layer, and a third global convolutional layer.
  • the first global convolutional layer includes a 1 ⁇ 1 ⁇ 256 Convolution kernel and a first global convolution with a step size of 2, a first global filling layer filled with 0, and a first global activation module.
  • the first global convolution layer includes a 1 ⁇ 1 ⁇ 128 convolution Kernel and a second global convolution with a step size of 2, a second global filling layer filled with 0, and a second global activation module.
  • the third global convolution layer includes a 1 ⁇ 1 ⁇ 1 convolution kernel and A third global convolution with a step size of 1 and a third global filling layer filled with 0.
  • the second vehicle damage feature is a feature of a common vector characteristic (also called a commonality) in all feature maps, and the second vehicle damage feature is performed on the global feature map through the first global convolution layer.
  • Extraction that is, extract the feature vector of the common vector feature from the global feature map to obtain the first global convolution feature map, input the first global convolution feature map to the second global convolution layer, and pass the The second global convolution layer performs the second vehicle damage feature extraction on the first global convolution feature map to obtain a second global convolution feature map, and inputting the second global convolution feature map to the first Three global convolutional layers, the second global convolution feature map is subjected to the second vehicle damage feature extraction through the third global convolutional layer to obtain the global feature vector map, and the second vehicle is extracted
  • the first global filling layer, the second global filling layer, and the third global filling layer are used to prevent interference by introducing common features, but can be filled to a preset size.
  • the Sigmoid activation layer uses a Sigmoid function as the last layer in the weak global feature adaptive model, and the Sigmoid function is a function that activates and classifies the global feature vector.
  • the activation process is a process of mapping a vector with a value of (- ⁇ , + ⁇ ) to a range of (0, 1) through the Sigmoid function, thereby obtaining the second adaptive feature vector group.
  • the global feature map is input into the first global convolutional layer, and the second vehicle damage feature of the global feature map is extracted through the first global convolution layer to obtain a global feature vector map;
  • the global feature vector map is input to the Sigmoid activation layer, and the global feature vector is activated through the Sigmoid activation layer to obtain a second adaptive feature vector group corresponding to the global feature map.
  • the second vehicle damage feature is extracted from the weaker feature vectors in all the global feature maps to prevent overfitting, extract high-quality second vehicle damage features, and provide second adaptation
  • the function of the feature vector group improves the accuracy and reliability of recognition.
  • the regularization process is regularization, that is, to increase rule restrictions, constrain optimization parameters, and prevent obvious features from being infinitely magnified to cause weakened features to be erased.
  • the damage types include scratches, scratches, dents, and dents. There are 7 types of damage, including wrinkles, dead-folds, tears, and missing.
  • the damage area is the area of the damaged position in the damaged image of the vehicle to be detected, that is, the distance of the damaged position relative to the damaged image of the vehicle to be detected. The full set of coordinate ranges.
  • One damage area corresponds to one damage type, and one damage type can correspond to multiple damage areas. In this way, after inputting the damage image of the vehicle to be detected, all damaged areas can be automatically identified Type, and the range of the damaged area corresponding to the type of damage.
  • This application obtains the damage image of the vehicle to be detected; inputs the damage image of the vehicle to be detected into an unsupervised domain adaptive network model; the unsupervised domain adaptive network model includes a pytorch-based migration learning model and a strong local feature adaptive model , Weak global feature adaptive model and regularization model; the pytorch-based migration learning model extracts the vehicle features of the vehicle damage image to be detected, and the pytorch-based migration learning model extracts the vehicle features in the process, A local feature map and a global feature map are generated; the vehicle feature is the feature related to the vehicle after the transfer learning; the transfer learning model based on pytorch outputs the transfer feature vector group according to the vehicle feature, and at the same time passes the strong The local feature adaptive model acquires the first adaptive feature vector group, and the second adaptive feature vector group is acquired through the weak global feature adaptive model; the transfer feature vector group and the first adaptive feature vector group are combined And the second adaptive feature vector group is input into the regularization model, and the migration feature vector group, the
  • an unsupervised domain adaptive network model suitable for vehicle damage detection is realized through the transfer learning pytorch model, the strong local feature adaptive model to strengthen the first vehicle damage feature and the weak global feature adaptive model to extract the second vehicle damage feature framework , It can quickly and accurately automatically identify the damage type and damage area corresponding to the damaged part of the vehicle in the damage image of the vehicle to be detected, greatly reducing the process of model construction and model training, and improving the type and damage assessment The accuracy and reliability of the area determination improves the efficiency of loss determination.
  • a vehicle damage feature detection device is provided, and the vehicle damage feature detection device corresponds to the vehicle damage feature detection method in the above-mentioned embodiment in a one-to-one correspondence.
  • the vehicle damage feature detection device includes a receiving module 11, an input module 12, an extraction module 13, an output module 14 and an identification module 15.
  • the detailed description of each functional module is as follows:
  • the receiving module 11 is configured to obtain a damage image of the vehicle to be detected after receiving a vehicle damage detection instruction; the damage image of the vehicle to be detected includes at least one image of the damaged location of the vehicle;
  • the input module 12 is used to input the to-be-detected vehicle damage image into an unsupervised domain adaptive network model;
  • the unsupervised domain adaptive network model includes a pytorch-based migration learning model, a strong local feature adaptive model, and a weak global feature Adaptive model and regularization model;
  • the extraction module 13 is used to extract the vehicle features of the damaged image of the vehicle to be detected through the pytorch-based migration learning model and generate a local feature map and a global feature map; the vehicle features are features related to the vehicle after the migration learning ;
  • the output module 14 is configured to output a transfer feature vector set according to the vehicle characteristics through the pytorch-based transfer learning model, and at the same time obtain a first adaptive feature vector set through the strong local feature adaptive model, and use the weak
  • the global feature adaptive model acquires a second adaptive feature vector group;
  • the first adaptive feature vector group is the strong local feature adaptive model acquired and output according to the first vehicle damage feature extracted from the local feature map
  • the second adaptive feature vector group is the weak global feature adaptive model obtained and output according to the second vehicle damage feature extracted from the global feature map;
  • the recognition module 15 is configured to input the migration feature vector group, the first adaptive feature vector group, and the second adaptive feature vector group into the regularization model, and use the regularization model to analyze the migration
  • the feature vector group, the first adaptive feature vector group, and the second adaptive feature vector group are subjected to regularization processing to obtain a recognition result including the damage type and the damage area; the recognition result represents the vehicle to be detected
  • the damage image contains the results of all damaged types and corresponding damaged areas.
  • the output module 14 includes:
  • the convolution unit 41 is configured to input the local feature map into a local convolution layer in the strong local feature adaptive model, and extract the first vehicle damage in the local feature map through the local convolution layer Feature, get the local feature vector diagram;
  • the pooling unit 42 is configured to input the local feature vector map into the pooling layer in the strong local feature adaptive model, and perform pooling processing on the local feature vector map through the pooling layer to obtain a local pool Matrix
  • the fully connected unit 43 is configured to input the local pooling matrix into the fully connected layer in the strong local feature adaptive model, and perform feature connection on the local pooling matrix through the fully connected layer to obtain a local connected matrix ;
  • the regression unit 44 is configured to input the local connection matrix into the Softmax layer in the strong local feature adaptive model, and perform regression processing on the local connection matrix through the Softmax layer to obtain the first local feature map corresponding to the local feature map.
  • An adaptive feature vector group is configured to input the local connection matrix into the Softmax layer in the strong local feature adaptive model, and perform regression processing on the local connection matrix through the Softmax layer to obtain the first local feature map corresponding to the local feature map.
  • each module in the above-mentioned vehicle damage feature detection device can be implemented in whole or in part by software, hardware, and a combination thereof.
  • the above-mentioned modules may be embedded in the form of hardware or independent of the processor in the computer equipment, or may be stored in the memory of the computer equipment in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
  • a computer device is provided.
  • the computer device may be a server, and its internal structure diagram may be as shown in FIG. 9.
  • the computer equipment includes a processor, a memory, a network interface, and a database connected through a system bus.
  • the processor of the computer device is used to provide calculation and control capabilities.
  • the memory of the computer device includes a readable storage medium and an internal memory.
  • the readable storage medium stores an operating system, computer readable instructions, and a database.
  • the internal memory provides an environment for the operation of the operating system and computer readable instructions in the readable storage medium.
  • the network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer-readable instruction is executed by the processor, a method for detecting damage characteristics of a vehicle is realized.
  • the readable storage medium provided in this embodiment includes a non-volatile readable storage medium and a volatile readable storage medium.
  • a computer device including a memory, a processor, and computer-readable instructions stored in the memory and capable of running on the processor.
  • the processor executes the computer-readable instructions, the vehicle in the foregoing embodiment is implemented. Damage feature detection method.
  • one or more readable storage media storing computer readable instructions are provided.
  • the readable storage media provided in this embodiment include non-volatile readable storage media and volatile readable storage. Medium; the readable storage medium stores computer readable instructions, and when the computer readable instructions are executed by one or more processors, the one or more processors implement the vehicle damage feature detection method in the foregoing embodiment.
  • Non-volatile memory may include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory.
  • Volatile memory may include random access memory (RAM) or external cache memory.
  • RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Channel (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
  • the blockchain referred to in this application is a new application mode of computer technology such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm.
  • Blockchain essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information for verification. The validity of the information (anti-counterfeiting) and the generation of the next block.
  • the blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • General Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Computing Systems (AREA)
  • Molecular Biology (AREA)
  • Health & Medical Sciences (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • General Health & Medical Sciences (AREA)
  • Multimedia (AREA)
  • Finance (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Accounting & Taxation (AREA)
  • Evolutionary Biology (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • Marketing (AREA)
  • Strategic Management (AREA)
  • Technology Law (AREA)
  • General Business, Economics & Management (AREA)
  • Image Analysis (AREA)

Abstract

L'invention concerne un procédé et un appareil de détection de caractéristiques d'endommagement de véhicule, un dispositif informatique et un support de stockage, le procédé comprenant les étapes consistant : à acquérir une image d'endommagement de véhicule à détecter (S10) et à l'entrer dans un modèle de réseau adaptatif de domaine non supervisé (S20) ; au moyen d'un modèle d'apprentissage par transfert basé sur PyTorch, à extraire des caractéristiques de véhicule et à générer une carte de caractéristiques locales et une carte de caractéristiques globales (S30) ; sur la base des caractéristiques du véhicule, à délivrer un groupe de vecteurs de caractéristiques de transfert et à acquérir simultanément un premier groupe de vecteurs de caractéristiques adaptatives au moyen d'un modèle adaptatif de caractéristiques locales fortes et à acquérir un second groupe vectoriel de caractéristiques adaptatives au moyen d'un modèle adaptatif de caractéristiques globales fortes (S40) ; et à réaliser un traitement de régularisation sur le groupe de vecteurs de caractéristiques de transfert, le premier groupe de vecteurs de caractéristiques adaptatives et le second groupe de vecteurs de caractéristiques adaptatives pour obtenir un résultat de reconnaissance (S50). La présente solution met en œuvre une reconnaissance automatique de types d'endommagement et de zones d'endommagement dans l'image d'endommagement de véhicule à détecter. La présente solution concerne également une technologie de chaîne de blocs ; le modèle adaptatif de domaine non supervisé peut être stocké dans la chaîne de blocs.
PCT/CN2020/116741 2020-05-27 2020-09-22 Procédé et appareil de détection d'endommagement de véhicule, dispositif informatique et support de stockage WO2021114809A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010462160.3A CN111666990A (zh) 2020-05-27 2020-05-27 车辆损伤特征检测方法、装置、计算机设备及存储介质
CN202010462160.3 2020-05-27

Publications (1)

Publication Number Publication Date
WO2021114809A1 true WO2021114809A1 (fr) 2021-06-17

Family

ID=72384809

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/116741 WO2021114809A1 (fr) 2020-05-27 2020-09-22 Procédé et appareil de détection d'endommagement de véhicule, dispositif informatique et support de stockage

Country Status (2)

Country Link
CN (1) CN111666990A (fr)
WO (1) WO2021114809A1 (fr)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113723356A (zh) * 2021-09-15 2021-11-30 北京航空航天大学 异质特征关系互补的车辆重识别方法和装置
CN116797533A (zh) * 2023-03-24 2023-09-22 东莞市冠锦电子科技有限公司 电源适配器的外观缺陷检测方法及其***
CN117447068A (zh) * 2023-10-26 2024-01-26 浙江欧诗漫晶体纤维有限公司 多晶莫来石纤维生产线及方法
CN117593301A (zh) * 2024-01-18 2024-02-23 深圳市奥斯珂科技有限公司 基于机器视觉的内存条损伤快速检测方法及***

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111666990A (zh) * 2020-05-27 2020-09-15 平安科技（深圳）有限公司 车辆损伤特征检测方法、装置、计算机设备及存储介质
CN112633373A (zh) * 2020-12-22 2021-04-09 东软睿驰汽车技术（沈阳）有限公司 一种车辆工况预测方法及装置
CN112907576B (zh) * 2021-03-25 2024-02-02 平安科技（深圳）有限公司 车辆损伤等级检测方法、装置、计算机设备及存储介质
CN113657409A (zh) * 2021-08-16 2021-11-16 平安科技（深圳）有限公司 车辆损失检测方法、装置、电子设备及存储介质
CN115115611B (zh) * 2022-07-21 2023-04-07 明觉科技（北京）有限公司 车辆损伤识别方法、装置、电子设备和存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108446618A (zh) * 2018-03-09 2018-08-24 平安科技（深圳）有限公司 车辆定损方法、装置、电子设备及存储介质
CN108734702A (zh) * 2018-04-26 2018-11-02 平安科技（深圳）有限公司 车损判定方法、服务器及存储介质
CN110570316A (zh) * 2018-08-31 2019-12-13 阿里巴巴集团控股有限公司 训练损伤识别模型的方法及装置
CN111666990A (zh) * 2020-05-27 2020-09-15 平安科技（深圳）有限公司 车辆损伤特征检测方法、装置、计算机设备及存储介质

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108446618A (zh) * 2018-03-09 2018-08-24 平安科技（深圳）有限公司 车辆定损方法、装置、电子设备及存储介质
CN108734702A (zh) * 2018-04-26 2018-11-02 平安科技（深圳）有限公司 车损判定方法、服务器及存储介质
CN110570316A (zh) * 2018-08-31 2019-12-13 阿里巴巴集团控股有限公司 训练损伤识别模型的方法及装置
CN111666990A (zh) * 2020-05-27 2020-09-15 平安科技（深圳）有限公司 车辆损伤特征检测方法、装置、计算机设备及存储介质

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
KUNIAKI SAITO, YOSHITAKA USHIKU, TATSUYA HARADA, KATE SAENKO: "Strong-Weak Distribution Alignment for Adaptive Object Detection", 2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 15 June 2019 (2019-06-15), pages 6956 - 6965, XP033687423, DOI: 10.1109/CVPR.2019.00712 *

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113723356A (zh) * 2021-09-15 2021-11-30 北京航空航天大学 异质特征关系互补的车辆重识别方法和装置
CN113723356B (zh) * 2021-09-15 2023-09-19 北京航空航天大学 异质特征关系互补的车辆重识别方法和装置
CN116797533A (zh) * 2023-03-24 2023-09-22 东莞市冠锦电子科技有限公司 电源适配器的外观缺陷检测方法及其***
CN116797533B (zh) * 2023-03-24 2024-01-23 东莞市冠锦电子科技有限公司 电源适配器的外观缺陷检测方法及其***
CN117447068A (zh) * 2023-10-26 2024-01-26 浙江欧诗漫晶体纤维有限公司 多晶莫来石纤维生产线及方法
CN117593301A (zh) * 2024-01-18 2024-02-23 深圳市奥斯珂科技有限公司 基于机器视觉的内存条损伤快速检测方法及***
CN117593301B (zh) * 2024-01-18 2024-04-30 深圳市奥斯珂科技有限公司 基于机器视觉的内存条损伤快速检测方法及***

Also Published As

Publication number Publication date
CN111666990A (zh) 2020-09-15

Similar Documents

Publication Publication Date Title
WO2021114809A1 (fr) Procédé et appareil de détection d'endommagement de véhicule, dispositif informatique et support de stockage
WO2021135499A1 (fr) Procédés d'entraînement de modèle de détection de dommages et de détection de dommages de véhicule, dispositif, appareil et support
CN111860670B (zh) 域自适应模型训练、图像检测方法、装置、设备及介质
WO2021017261A1 (fr) Procédé et appareil d'entraînement de modèles de reconnaissance, procédé et appareil de reconnaissance d'images, et dispositif et support
WO2021121127A1 (fr) Procédé d'identification de type d'échantillon, appareil, dispositif informatique et support de stockage
WO2021135500A1 (fr) Appareil et procédé d'entraînement de modèle de détection de perte de véhicule, appareil et procédé de détection de perte de véhicule, et dispositif et support
CN110807491A (zh) 车牌图像清晰度模型训练方法、清晰度检测方法及装置
CN109840524B (zh) 文字的类型识别方法、装置、设备及存储介质
CN107886082B (zh) 图像中数学公式检测方法、装置、计算机设备及存储介质
WO2022134354A1 (fr) Procédé et appareil d'entraînement de modèle de détection de perte de véhicule, procédé et appareil de détection de perte de véhicule, dispositif et support
CN111144285B (zh) 胖瘦程度识别方法、装置、设备及介质
CN113705685B (zh) 疾病特征识别模型训练、疾病特征识别方法、装置及设备
CN115797735A (zh) 目标检测方法、装置、设备和存储介质
CN110728680A (zh) 行车记录仪检测方法、装置、计算机设备和存储介质
CN109101984B (zh) 一种基于卷积神经网络的图像识别方法及装置
CN110276802B (zh) 医学图像中病症组织定位方法、装置与设备
CN112241705A (zh) 基于分类回归的目标检测模型训练方法和目标检测方法
CN110751623A (zh) 基于联合特征的缺陷检测方法、装置、设备及存储介质
CN116091596A (zh) 一种自下而上的多人2d人体姿态估计方法及装置
CN111666973B (zh) 车辆损伤图片处理方法、装置、计算机设备及存储介质
CN114332915A (zh) 人体属性检测方法、装置、计算机设备及存储介质
CN115797990A (zh) 图像分类、图像处理方法、装置和存储介质
CN110956102A (zh) 银行柜台监控方法、装置、计算机设备和存储介质
CN112347893B (zh) 用于视频行为识别的模型训练方法、装置和计算机设备
CN116952954B (zh) 一种基于条纹光的凹凸检测方法、装置、设备及存储介质

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20899826

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20899826

Country of ref document: EP

Kind code of ref document: A1