WO2022088720A1 - 样本生成、神经网络的训练、数据处理方法及装置 - Google Patents
样本生成、神经网络的训练、数据处理方法及装置 Download PDFInfo
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Definitions
- the present disclosure relates to the technical field of machine learning, and in particular, to a method, device, computer equipment and storage medium for sample generation, neural network training, data processing, and driving control of an intelligent driving device.
- object detection neural networks are widely used in fields such as autonomous driving and robotic handling.
- autonomous driving after using lidar to collect data from the target scene, the obtained point cloud data can be marked, and the marked point cloud data can be used to train the target detection neural network; the target detection neural network can be used for automatic Obstacle detection during driving.
- the current target detection neural network has the problem of low detection accuracy during training.
- the embodiments of the present disclosure provide at least a method, device, computer equipment, and storage medium for sample generation, neural network training, data processing, and driving control of an intelligent driving device.
- an embodiment of the present disclosure provides a sample generation method, including:
- the first confidence threshold characterizing the existence of the target in the point cloud data
- the second confidence threshold characterizing the absence of the target in the point cloud data
- Sample data is generated based on the first target point cloud data and a first target detection result corresponding to the first target point cloud data.
- the reliability of the generated sample data can be improved, thereby improving the detection accuracy of the target detection model obtained after training.
- the first target detection result includes: the confidence level of the target in the first point cloud data of each frame; the first confidence level threshold is greater than the second confidence level threshold;
- determining the first target point cloud data includes:
- the first point cloud data including the target whose confidence is greater than the first confidence threshold or smaller than the second confidence threshold is determined as the first target point cloud data.
- the first point cloud data can be screened by using the first probability threshold and the second probability threshold used to characterize the possibility of determining the existence of the target object in the first point cloud data, and ignoring the part cannot accurately determine whether the target detection result is Therefore, the classification accuracy of the first target point cloud data can be improved.
- a pre-trained target detection neural network is used to perform target detection on each frame of the first point cloud data in the multi-frame first point cloud data, based on the first target point cloud data, and the first target detection result of the first target point cloud data to generate sample data, including:
- the pre-trained target detection neural network is iteratively trained; after using the first target point cloud data , and the first target detection result of the first target point cloud data, after performing k rounds of iterative training on the pre-trained target detection neural network, the trained target detection neural network is obtained; k is a positive integer;
- the sample data is generated based on the second target detection result of the first point cloud data of each frame.
- the obtained trained target detection neural network learns the features in the first target point cloud data. Therefore, using the trained target The detection neural network then performs target detection processing on the first point cloud data, which has higher accuracy than the pre-trained target detection neural network.
- it also includes: in the case where the loop stop condition is not met, based on the second target detection result of the first point cloud data of each frame, the first confidence threshold, and the The second confidence threshold is to determine the second target point cloud data from the multiple frames of the first point cloud data;
- the target detection results of the first point cloud data are continuously updated, and during the update process, the accuracy is continuously improved, so that the final sample data has a higher labeling accuracy.
- the cycle stop condition includes at least one of the following:
- the number of times of obtaining the trained target detection neural network reaches a preset number of times; the preset number of times is an integer multiple of k;
- the similarity between the first target detection result and the second target detection result of the first point cloud data of each frame is greater than the preset similarity threshold.
- it also includes:
- the generating sample data based on the first target point cloud data and the first target detection result of the first target point cloud data includes:
- the first target detection result of the first target point cloud data, the third target point cloud data, and the third target detection result of the third target point cloud data Based on the first target point cloud data, the first target detection result of the first target point cloud data, the third target point cloud data, and the third target detection result of the third target point cloud data, generating the sample data.
- the influence on the training of the target detection neural network can be avoided when the data amount of the first target point cloud data is small; or, the trained target detection neural network can have stronger generalization ability .
- the data enhancement processing includes at least one of the following:
- an embodiment of the present disclosure provides a method for training a neural network, including:
- the target detection neural network to be trained is trained to obtain the trained target detection neural network.
- an embodiment of the present disclosure provides a data processing method, including:
- the point cloud data to be processed is processed to obtain a data processing result of the point cloud data to be processed.
- an embodiment of the present disclosure provides a driving control method for an intelligent driving device, including:
- the intelligent driving device is controlled.
- an embodiment of the present disclosure further provides a sample generation device, including:
- a first detection module configured to perform target detection on each frame of the first point cloud data in the multiple frames of the first point cloud data, and obtain a first target detection result of the first point cloud data in each frame;
- a determination module for detecting a first target based on the first point cloud data of each frame, a first confidence threshold characterizing the existence of a target in the point cloud data, and a second confidence characterizing the absence of a target in the point cloud data a threshold, from the multi-frame first point cloud data, to determine the first target point cloud data;
- the first generation module is configured to generate sample data based on the first target point cloud data and the first target detection result corresponding to the first target point cloud data.
- an embodiment of the present disclosure further provides a training device for a neural network, including:
- a second generation module configured to generate sample data by using the sample generation method described in the first aspect or any optional implementation manner of the first aspect of the embodiments of the present disclosure
- the model training module is used for using the sample data to train the target detection neural network to be trained to obtain the trained target detection neural network.
- an embodiment of the present disclosure further provides a data processing apparatus, including:
- the first acquisition module is used to acquire point cloud data to be processed
- a processing module configured to process the point cloud data to be processed by using the neural network trained based on the neural network training method described in any one of the second aspects to obtain data processing of the point cloud data to be processed result.
- an embodiment of the present disclosure further provides a driving control device for an intelligent driving device, including:
- the second acquisition module is used for acquiring point cloud data collected by the intelligent driving device during driving;
- a second detection module configured to detect the target object in the point cloud data using the neural network trained by the neural network training method according to any one of the second aspects
- the control module is used for controlling the intelligent driving device based on the detected target object.
- an optional implementation manner of the present disclosure further provides a computer device, a processor, and a memory, where the memory stores machine-readable instructions executable by the processor, and the processor is configured to execute the memory stored in the memory
- the machine-readable instructions when the machine-readable instructions are executed by the processor, the machine-readable instructions when executed by the processor perform the above-mentioned first aspect, second aspect, third aspect or fourth aspect steps in any of the possible implementations.
- an optional implementation manner of the present disclosure further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program executes the first aspect, the second aspect, and the third aspect when the computer program is run.
- FIG. 1 shows a flowchart of a sample generation method provided by an embodiment of the present disclosure
- FIG. 2 shows a flowchart of a specific method for generating sample data based on the determined first target point cloud data and the first target detection result corresponding to the first target point cloud data provided by an embodiment of the present disclosure
- FIG. 3 shows a flowchart of a method for training a neural network provided by an embodiment of the present disclosure
- FIG. 4 shows a flowchart of a data processing method provided by an embodiment of the present disclosure
- FIG. 5 shows a flowchart of a driving control method of an intelligent driving device provided by an embodiment of the present disclosure
- FIG. 6 shows a schematic diagram of a sample generating apparatus provided by an embodiment of the present disclosure
- FIG. 7 shows a schematic diagram of a training apparatus for a neural network provided by an embodiment of the present disclosure
- FIG. 8 shows a schematic diagram of a data processing apparatus provided by an embodiment of the present disclosure
- FIG. 9 shows a schematic diagram of a driving control device of an intelligent driving device provided by an embodiment of the present disclosure.
- FIG. 10 shows a schematic diagram of the structure of a computer device provided by an embodiment of the present disclosure.
- the execution subject of the sample generation method provided by the embodiment of the present disclosure is generally a device with a certain computing capability, such as Including: terminal equipment or server or other processing equipment, terminal equipment can be user equipment (User Equipment, UE), mobile equipment, user terminal, terminal, cellular phone, cordless phone, Personal Digital Assistant (Personal Digital Assistant, PDA), handheld devices, computing devices, in-vehicle devices, wearable devices, etc.
- terminal equipment can be user equipment (User Equipment, UE), mobile equipment, user terminal, terminal, cellular phone, cordless phone, Personal Digital Assistant (Personal Digital Assistant, PDA), handheld devices, computing devices, in-vehicle devices, wearable devices, etc.
- the sample generation method may be implemented by a processor invoking computer-readable instructions stored in a memory.
- the sample generation method includes steps S101-S103, wherein:
- S101 Perform target detection on each frame of the first point cloud data in the multiple frames of the first point cloud data, and obtain a first target detection result of the first point cloud data in each frame;
- S102 Based on the first target detection result of the first point cloud data of each frame, the first confidence threshold representing the existence of the target in the point cloud data, and the second confidence threshold representing the absence of the target in the point cloud data, from the multi-frame In the first point cloud data, determine the first target point cloud data;
- S103 Generate sample data based on the first target point cloud data and the first target detection result corresponding to the first target point cloud data.
- a preset first confidence threshold representing the existence of a target in the first point cloud data and a first confidence threshold representing the first point are used.
- the second confidence threshold that the target does not exist in the cloud data determines the first target point cloud data, and then uses the first target point cloud data and its corresponding first target detection result to generate sample data; after determining the first target point cloud data During the process, select the first point cloud data with a higher target confidence (eg, closer to 1) in the first target detection result, or select the first target detection result with a lower target confidence (eg, closer to 0)
- the first point cloud data is used as the first target point cloud data, and the first point cloud data whose target confidence is closer to the intermediate value (for example, a value between 0 and 1) in the first target detection result is not selected as the first target point Cloud data, thereby increasing the reliability of the generated sample data.
- the first point cloud data may be, for example, point cloud data obtained by collecting the first target space by using at least one collection device of a radar, a depth camera, a color camera, or the like.
- the target space may contain objects such as obstacles.
- the radar when a radar is used to acquire point cloud data in the target space, the radar can transmit a detection signal, detect the target space, and obtain the first point cloud data in the target space based on the detection result.
- one or more of structured light, binocular vision, light time-of-flight method, etc. can be used to obtain the depth image of the target space, and then based on the depth image, the target space can be obtained.
- the color camera can collect a two-dimensional image of the target space; reconstruct the three-dimensional space based on the two-dimensional image to obtain the first point cloud data of the target space.
- the embodiments of the present disclosure are described by using radar to obtain the first point cloud data of the target space.
- the pre-trained target detection neural network includes, for example, a Bayesian neural network. , BN) or artificial neural network (Artificial Neural Network, ANN).
- BN Bayesian neural network
- ANN Artificial Neural Network
- the second point cloud data can be acquired first, and the acquired second point cloud data usually has label information; here, the radar that acquires the second point cloud data, for example, can be obtained with the same method as acquiring the first point cloud data.
- the radars of the data are different; among them, it can be at least one of different radar parameters, different radar types, different radar installation postures, different radar application areas, etc. The details are not repeated here.
- the labeling information may include, for example, "obstacle” and "non-obstacle", and in the case of an obstacle, the position information of the obstacle in the second point cloud data (for example, the labeling frame corresponding to the obstacle is at the second point. coordinates in cloud data), obstacle size, obstacle class, and a confidence score for that class.
- the first target detection result of the first point cloud data obtained by using the pre-trained target detection neural network also includes: the coordinates of the target in the first point cloud data, the target size, the obstacle category to which the target belongs, and the A confidence score for a category; here, the confidence score may, for example, be in the form of a predicted probability.
- a pre-trained target detection neural network can be obtained by training the second point cloud data with label information.
- the target detection neural network pre-trained by the second point cloud data has good processing performance for the second point cloud data; the pre-trained target detection neural network is used to detect the first point cloud of each frame of the multi-frame first point cloud data.
- the data is subjected to target detection processing, and the first target detection result corresponding to the first point cloud data of each frame is obtained.
- the pre-trained target detection neural network since the pre-trained target detection neural network is obtained by training using the second point cloud data with label information, it has good processing performance for point cloud data with similar feature distribution to the second point cloud data; but Since the first point cloud data and the second point cloud data have a certain difference in the feature domain, the target detection network pre-trained based on the second point cloud data processes the first point cloud data, and obtains the corresponding first point cloud data.
- the first target detection result is , there is a certain difference between the first target prediction result and the real target detection result corresponding to the first point cloud data.
- the first point cloud data should be screened based on S102 of the present disclosure, and the first target point should be determined from the multiple frames of the first point cloud data cloud data.
- the preset first confidence threshold for characterizing the existence of the target in the first point cloud data and the non-existence in the first point cloud data may be used.
- the second confidence threshold of the target is to determine the first target point cloud data with higher confidence in the classification result from the multi-frame first point cloud data.
- the first confidence threshold and the second confidence threshold are used to represent the possibility of determining the existence/absence of the target in the first point cloud data; when screening the first target point cloud data from the first point cloud data , you can select the first point cloud data with higher target confidence (eg, closer to 1) in the first target detection result, or select the first target detection result with lower target confidence (eg, closer to 0)
- the first point cloud data The point cloud data is used as the first target point cloud data, and the first point cloud data whose target confidence is closer to the intermediate value in the first target detection result is not selected as the first target point cloud data, thereby improving the reliability of the generated sample data. sex.
- the first confidence threshold is higher than the second confidence threshold
- the first confidence threshold may be represented as P 1
- the second confidence threshold may be represented as P 2 , for example.
- the first confidence threshold P 1 may be set to 70%
- the second confidence threshold P 2 may be set to 30%, that is to say, it is considered that there must be no existence when the confidence of the first target detection result is lower than 30%. The target must exist when the confidence of the first target detection result exceeds 70%.
- first confidence threshold and second confidence threshold are all examples.
- the specific values of the first confidence threshold and the second confidence threshold it can be set according to experience, or according to the target detection result.
- the accuracy requirements are determined, and the specifics can be determined according to the actual situation, which will not be repeated here.
- the following methods when determining the first confidence threshold and the second confidence threshold, when determining the first target point cloud data from multiple frames of first point cloud data, for example, the following methods may be used:
- the first point cloud data of the target of the second confidence threshold is determined as the first target point cloud data.
- the confidence levels of targets in different first point cloud data of N frames can be Denoted as p i ,i ⁇ [1,N].
- the results obtained by comparing the confidence level p i with the first confidence level threshold P 1 and the second confidence level threshold P 2 include the following one: kind:
- the point cloud data of the i -th frame must not include the target; in the case of P 1 ⁇ pi, it is considered that the point cloud data of the i-th frame must include the target;
- the i-frame point cloud data is determined as the first target point cloud data.
- the point cloud data of the ith frame is determined as the buffer domain (that is, the confidence level is located in the first confidence level). point cloud data in the region between the threshold and the second confidence threshold).
- the target detection result obtained based on the first target point cloud data is more accurate.
- the multi-frame first target point cloud data screened from the multi-frame first point cloud data can more accurately determine whether the target contains the target. Therefore, when training the target detection neural network based on the first target point cloud data, due to The reliability of the first target detection results generated for the first target point cloud data is relatively high, and the negative impact of the point cloud data with low reliability of the detection results on the target detection neural network can be excluded, so that the target detection neural network can be eliminated. with higher precision.
- S1031 Perform iterative training on the pre-trained target detection neural network by using the first target point cloud data and the first target detection result of the first target point cloud data.
- S1033 Use the trained target detection neural network to determine a second target detection result of each frame of the first point cloud data in the multiple frames of the first point cloud data.
- S1034 Determine whether the loop stop condition is satisfied; if so, jump to S1037, and if not, jump to S1035.
- S1035 Based on the second target detection result, the first confidence threshold, and the second confidence threshold of the first point cloud data of each frame, determine the second target point cloud data from the multiple frames of the first point cloud data.
- S1036 Use the second target point cloud data as new first target point cloud data, and use the second target detection result of the second target point cloud data as a new first target of the new first target point cloud data
- the detection result and the training of the target detection neural network as the pre-trained target detection neural network are returned to S1031.
- S1037 Generate sample data based on the second target detection result of the first point cloud data of each frame.
- the pre-trained target detection neural network can be obtained by training the second point cloud data with label information. Since the second point cloud data and the first point cloud data may belong to different radar data sets, if the pre-trained target detection neural network is directly used for target detection on the first point cloud data, the obtained processing results may be different from the actual ones. There are deviations.
- the first point cloud data may be screened to obtain the first target point cloud data, and then the pre-trained target detection neural network may be trained by using the first target point cloud data. , the trained target detection neural network can learn the features in the first target point cloud data. Therefore, using the trained target detection neural network to perform target detection processing on the first point cloud data, compared with the pre-trained target The detection neural network has higher accuracy.
- the above-mentioned loop stop condition may include that the number of times of obtaining the trained target detection neural network reaches a preset number of times.
- the number of times of obtaining the trained target detection neural network may be increased by 1 each time iterative training is performed.
- the preset times are, for example, 5 times, 7 times, and 10 times. When the preset number of times is small, the number of iterations is small, and the target detection neural network can be obtained by training faster within the allowable error range; when the preset number of times is large, more accurate target detection can be determined.
- Neural network for object detection may include that the number of times of obtaining the trained target detection neural network reaches a preset number of times.
- the above-mentioned loop stop condition may include that the preset number of times is an integer multiple of k, and the preset number of times is, for example, N ⁇ k times, where N is a positive integer.
- N can be set to a large positive integer, such as 5.
- N can be set to a small positive integer, such as 2 or 3.
- the specific preset number of times can be determined according to the actual situation, and details are not repeated here.
- the above-mentioned loop stop condition may include: the similarity between the first target detection result and the second target detection result of the first point cloud data in each frame is greater than a preset similarity threshold.
- the target detection results of the first point cloud data are finally continuously updated, and during the update process, the accuracy is continuously improved, so that the final sample data has high labeling accuracy.
- the second target detection result since the second target detection result is more accurate than the first target detection result obtained most recently, the second target point cloud data and the After the first confidence threshold and the second confidence threshold are compared, the data volume of the obtained new first target point cloud data may increase, so that there are more abundant training samples in the next training of the target detection neural network; Or, when using the first confidence threshold and the second confidence threshold to determine the first target point cloud data, the confidence of the target is located between the first confidence threshold and the second confidence threshold. The number is reduced, that is, when the first target detection result is used to determine whether there is a target at the corresponding position in the first point cloud data, the reliability is higher.
- data enhancement processing can also be performed on the first target point cloud data to generate third target point cloud data, and based on the first target detection result corresponding to the first point cloud data, a third target point cloud is generated.
- the third object detection result of the data is generated.
- the data enhancement processing includes at least one of the following: random rotation scene processing, random scene flipping processing along the coordinate axis, random object scaling processing, random object rotation processing, and random sampling point cloud processing along the coordinate axis.
- the random rotation scene processing includes, for example, rotating the coordinate axis corresponding to some point cloud data in the first target point cloud data, and determining the new coordinate value corresponding to this part of the point cloud point based on the coordinate axis obtained after the rotation, and using the new coordinate value to update
- the first target point cloud data determines the third target point cloud data.
- the first target detection result of the cloud data is adjusted to generate a third target detection result of the third target point cloud data.
- the method of generating sample data by using other data enhancement methods is similar to the above-mentioned method of generating sample data by using the random rotation scene processing method, and will not be repeated here.
- the sample data is generated based on the first target point cloud data, the first target detection result corresponding to the first target point cloud data, the third target point cloud data, and the third target detection result corresponding to the third target point cloud data.
- the specific sample data is generated.
- Ways for example, can include:
- the training The pre-trained target detection neural network is obtained, and the trained target detection neural network is obtained;
- the sample data is generated based on the second target detection results corresponding to the multiple frames of the first point cloud data; or, based on the multiple frames of the first point cloud data corresponding to The second target detection result and the third target detection results corresponding to the multi-frame third target point cloud data respectively, generate sample data.
- the embodiments of the present disclosure also provide a neural network training method corresponding to the sample generation method.
- the training method includes steps S301 to S304 , wherein:
- S301 Perform target detection on each frame of the first point cloud data in the multiple frames of the first point cloud data, to obtain a first target detection result of the first point cloud data in each frame;
- S302 Based on the first target detection result of the first point cloud data of each frame, the first confidence threshold representing the existence of the target in the point cloud data, and the second confidence threshold representing the absence of the target in the point cloud data, from the multi-frame In the first point cloud data, determine the first target point cloud data;
- S303 Generate sample data based on the first target point cloud data and the first target detection result corresponding to the first target point cloud data;
- the target detection neural network may be the same as the pre-trained target detection neural network in the above-mentioned sample generation method, or a new target detection neural network is selected for training.
- the target detection neural network may include, for example, a Bayesian neural network (Bayesian Network, BN) or an artificial neural network (Artificial Neural Network, ANN).
- BN Bayesian neural network
- ANN Artificial Neural Network
- the target detection neural network to be trained can be trained to obtain the target detection neural network.
- the specific method for generating sample data corresponding to the above S301-S303 is similar to the sample generating method corresponding to the above-mentioned S101-S103, and details are not repeated here.
- the embodiments of the present disclosure also provide a data processing method corresponding to the sample generation method.
- the data processing method includes steps S5-S402, wherein:
- the point cloud data to be processed may include, for example, first point cloud data, or point cloud data without label information.
- the specific method for acquiring the point cloud data to be processed is similar to the method for acquiring the first point cloud data in the above S101, and details are not repeated here.
- the obtained data processing result of the point cloud data to be processed may include, for example, the target detection result corresponding to the point cloud data to be processed, That is, it is determined for the point cloud data to be processed whether the corresponding position contains the label information of the target object.
- the target detection result obtained by the obtained target detection neural network when performing target detection on any point cloud data is more accurate, the data obtained after target detection processing is performed on the point cloud data to be processed by the target detection neural network. The accuracy of the processing results is higher.
- the embodiment of the present disclosure also provides a driving control method of an intelligent driving device corresponding to the sample generation method.
- the driving method for an intelligent driving device includes steps S501 to S503 , wherein:
- S501 Acquire point cloud data collected by the intelligent driving device during driving
- S502 Use the neural network trained by the neural network training method provided by the embodiment of the present disclosure to detect the target object in the point cloud data;
- the driving device is, for example, but not limited to, any one of the following: an autonomous vehicle, a vehicle equipped with an advanced driving assistance system (Advanced Driving Assistance System, ADAS), or a robot, and the like.
- an autonomous vehicle a vehicle equipped with an advanced driving assistance system (Advanced Driving Assistance System, ADAS), or a robot, and the like.
- ADAS Advanced Driving Assistance System
- robot a robot, and the like.
- Controlling the traveling device includes, for example, controlling the traveling device to accelerate, decelerate, turn, and brake, or play voice prompt information to prompt the driver to control the traveling device to accelerate, decelerate, turn, and brake.
- the specific position of the obstacle in the target space can be determined based on the target object, so as to control the intelligent driving device to avoid the obstacle in the target space;
- the specific position of the road that can be driven in the target space can be determined based on the target object, so as to control the intelligent driving device to drive within the range of the road that can be driven.
- the target detection neural network obtained by using the neural network training method has higher accuracy, when the target detection neural network obtained by using the neural network training method is used to perform target detection on the point cloud data to be processed , the obtained target detection result is more accurate, so that there is a more accurate judgment result when judging whether there is an obstacle in the target space, so that the ability to avoid obstacles when controlling the intelligent driving device to drive is stronger, and the safety is higher.
- the writing order of each step does not mean a strict execution order but constitutes any limitation on the implementation process, and the specific execution order of each step should be based on its function and possible Internal logic is determined.
- the embodiment of the present disclosure also provides a sample generation device corresponding to the sample generation method. Reference may be made to the implementation of the method, and repeated descriptions will not be repeated.
- the device includes: a first detection module 61 , a determination module 62 , and a first generation module 63 ; wherein,
- the first detection module 61 is configured to perform target detection on each frame of the first point cloud data in the multiple frames of the first point cloud data, and obtain a first target detection result of the first point cloud data in each frame;
- a determination module 62 used for the first target detection result based on the first point cloud data of each frame, the first confidence threshold for characterizing the existence of the target in the point cloud data, and the second confidence characterizing the absence of the target in the point cloud data a degree threshold, from the multi-frame first point cloud data, to determine the first target point cloud data;
- the first generating module 63 is configured to generate sample data based on the first target point cloud data and the first target detection result corresponding to the first target point cloud data.
- the first target detection result includes: the confidence level of the target in the first point cloud data of each frame; the first confidence level threshold is greater than the second confidence level threshold;
- the determination module 62 is based on the first target detection result corresponding to the first point cloud data of each frame, the first confidence threshold representing the existence of the target in the point cloud data, and the first threshold representing the absence of the target in the point cloud data. Two confidence thresholds, which are used to determine the first target point cloud data from the multi-frame first point cloud data:
- the first point cloud data including the target whose confidence is greater than the first confidence threshold or smaller than the second confidence threshold is determined as the first target point cloud data.
- the first detection module 61 uses a pre-trained target detection neural network to perform target detection on each frame of the first point cloud data in the multiple frames of first point cloud data, and the first point cloud data
- a generating module 63 when generating sample data based on the first target point cloud data and the first target detection result of the first target point cloud data, is used for:
- the pre-trained target detection neural network is iteratively trained; after using the first target point cloud data , and the first target detection result of the first target point cloud data, after performing k rounds of iterative training on the pre-trained target detection neural network, the trained target detection neural network is obtained; k is a positive integer;
- the sample data is generated based on the second target detection result of the first point cloud data of each frame.
- the first generation module 63 is further configured to: in the case that the loop stop condition is not satisfied, based on the second target detection result of the first point cloud data of each frame, the a confidence threshold and the second confidence threshold, from the multi-frame first point cloud data, to determine the second target point cloud data;
- the cycle stop condition includes at least one of the following:
- the number of times of obtaining the trained target detection neural network reaches a preset number of times; the preset number of times is an integer multiple of k;
- the similarity between the first target detection result and the second target detection result of the first point cloud data in each frame is greater than a preset similarity threshold.
- it also includes a data enhancement processing module 64 for:
- the first generation module 63 when generating sample data based on the first target point cloud data and the first target detection result of the first target point cloud data, is used for:
- the first target detection result of the first target point cloud data, the third target point cloud data, and the third target detection result of the third target point cloud data Based on the first target point cloud data, the first target detection result of the first target point cloud data, the third target point cloud data, and the third target detection result of the third target point cloud data, generating the sample data.
- the data enhancement processing includes at least one of the following:
- the embodiment of the present disclosure also provides a sample generation device corresponding to the sample generation method. Reference may be made to the implementation of the method, and repeated descriptions will not be repeated.
- the apparatus includes: a second generation module 71 and a model training module 72 ; wherein,
- the second generation module 71 is configured to generate sample data by using any of the sample generation methods provided in the embodiments of the present disclosure
- the model training module 72 is configured to use the sample data to train the target detection neural network to be trained to obtain the trained target detection neural network.
- the embodiment of the present disclosure also provides a neural network training device corresponding to the neural network training method, because the principle of solving the problem by the device in the embodiment of the present disclosure and the above-mentioned neural network training method in the embodiment of the present disclosure Similar, therefore, the implementation of the apparatus may refer to the implementation of the method, and repeated descriptions will not be repeated.
- the apparatus includes: a first acquisition module 81 and a processing module 82 ; wherein,
- the first acquisition module 81 is used to acquire point cloud data to be processed
- the processing module 82 is configured to process the point cloud data to be processed by using the neural network trained based on any of the neural network training methods provided in the embodiments of the present disclosure to obtain data of the point cloud data to be processed process result.
- the embodiment of the present disclosure also provides a data processing apparatus corresponding to the data processing method. Reference may be made to the implementation of the method, and repeated descriptions will not be repeated.
- FIG. 9 is a schematic diagram of a driving control device of an intelligent driving device provided by an embodiment of the present disclosure
- the device includes: a second acquisition module 91 , a second detection module 92 , and a control module 93 ; wherein,
- the second acquisition module 91 is configured to acquire point cloud data collected by the intelligent driving device during driving;
- the second detection module 92 is configured to detect the target object in the point cloud data by using the neural network trained based on any one of the neural network training methods provided in the embodiments of the present disclosure;
- the control module 93 is configured to control the intelligent driving device based on the detected target object.
- An embodiment of the present disclosure also provides a computer device. As shown in FIG. 10 , a schematic diagram of the structure of the computer device provided by the embodiment of the present disclosure includes:
- a processor 10 and a memory 20 stores machine-readable instructions executable by the processor 10, the processor 10 is configured to execute the machine-readable instructions stored in the memory 20, and the machine-readable instructions are executed by the processor 10 When executed, the processor 10 performs the following steps:
- the first confidence threshold characterizing the existence of the target in the point cloud data
- the second confidence threshold characterizing the absence of the target in the point cloud data
- Sample data is generated based on the first target point cloud data and the first target detection result corresponding to the first target point cloud data.
- the processor 10 performs the following steps:
- the target detection neural network to be trained is trained, and the trained target detection neural network is obtained.
- the processor 10 performs the following steps:
- the neural network trained by any of the neural network training methods provided in the embodiments of the present disclosure processes the point cloud data to be processed to obtain a data processing result of the point cloud data to be processed.
- the processor 10 performs the following steps:
- the intelligent driving device is controlled.
- the above-mentioned memory 20 includes a memory 2021 and an external memory 2022; the memory 2021 here is also called an internal memory, which is used to temporarily store the operation data in the processor 10 and the data exchanged with the external memory 2022 such as the hard disk.
- the external memory 2022 performs data exchange.
- Embodiments of the present disclosure further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is run by a processor, the sample generation, neural Network training, data processing, and steps of a driving method for an intelligent driving device.
- the storage medium may be a volatile or non-volatile computer-readable storage medium.
- Embodiments of the present disclosure further provide a computer program product, where the computer program product carries program code, and the program code includes instructions that can be used to perform the sample generation and neural network training and training respectively corresponding to the above method embodiments , data processing, and the steps of the driving method of the intelligent driving device, for details, refer to the above method embodiments, which will not be repeated here.
- the computer program product can be specifically implemented by hardware, software or a combination thereof.
- the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (Software Development Kit, SDK), etc. Wait.
- the units described as separate components may or may not be physically separated, and components displayed as units may or may not be physical units, that is, may be located in one place, or may be distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution in this embodiment.
- each functional unit in each embodiment of the present disclosure may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit.
- the functions, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a processor-executable non-volatile computer-readable storage medium.
- the computer software products are stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present disclosure.
- the aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), magnetic disk or optical disk and other media that can store program codes .
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Abstract
Description
Claims (16)
- 一种样本生成方法,其特征在于,包括:对多帧第一点云数据中的每帧第一点云数据进行目标检测,得到每帧第一点云数据的第一目标检测结果;基于所述每帧第一点云数据的第一目标检测结果、表征点云数据中存在目标的第一置信度阈值、以及表征点云数据中不存在目标的第二置信度阈值,从所述多帧第一点云数据中,确定第一目标点云数据;基于所述第一目标点云数据、以及所述第一目标点云数据对应的第一目标检测结果,生成样本数据。
- 根据权利要求1所述的样本生成方法,其特征在于,所述第一目标检测结果包括:所述每帧第一点云数据中的目标的置信度;所述第一置信度阈值大于所述第二置信度阈值;基于所述每帧第一点云数据的第一目标检测结果、表征点云数据中存在目标的第一置信度阈值、以及表征点云数据中不存在目标的第二置信度阈值,从所述多帧第一点云数据中,确定第一目标点云数据,包括:将每帧第一点云数据中的目标的置信度分别与所述第一置信度阈值和所述第二置信度阈值进行比对;将包含置信度大于所述第一置信度阈值,或者小于所述第二置信度阈值的目标的第一点云数据确定为所述第一目标点云数据。
- 根据权利要求1所述的样本生成方法,其特征在于,利用预训练的目标检测神经网络对所述多帧第一点云数据中的每帧第一点云数据进行目标检测,基于所述第一目标点云数据、以及所述第一目标点云数据的第一目标检测结果,生成样本数据,包括:利用所述第一目标点云数据、以及所述第一目标点云数据的第一目标检测结果,对所述预训练的目标检测神经网络进行迭代训练;在利用所述第一目标点云数据、以及所述第一目标点云数据的第一目标检测结果,对所述预训练的目标检测神经网络进行k轮迭代训练之后,得到训练后的目标检测神经网络;k为正整数;利用所述训练后的目标检测神经网络,确定所述多帧第一点云数据中每帧第一点云数据的第二目标检测结果;在满足循环停止条件的情况下,基于每帧第一点云数据的第二目标检测结果,生成所述样本数据。
- 根据权利要求3所述的样本生成方法,其特征在于,还包括:在不满足循环停止条件的情况下,基于所述每帧第一点云数据的第二目标检测结果、所述第一置信度阈值、以及所述第二置信度阈值,从所述多帧第一点云数据中,确定第二目标点云数据;将第二目标点云数据作为新的第一目标点云数据,并将第二目标点云数据的第二目标检测结果作为新的第一目标点云数据的新的第一目标检测结果,以及将所述训练后的目标检测神经网络作为所述预训练的目标检测神经网络,返回至利用所述第一目标点云数据、以及所述第一目标点云数据的第一目标检测结果,对所述预训练的目标检测神经 网络进行迭代训练的步骤。
- 根据权利要求3或4所述的样本生成方法,其特征在于,所述循环停止条件包括下述至少一种:得到所述训练后的目标检测神经网络的次数达到预设次数;所述预设次数为k的整数倍;每帧第一点云数据的第一目标检测结果和第二目标检测结果之间的相似度,大于预设的相似度阈值。
- 根据权利要求1-5任一项所述的样本生成方法,其特征在于,还包括:对所述第一目标点云数据进行数据增强处理,生成第三目标点云数据,以及基于所述第一目标点云数据对应的第一目标检测结果,生成所述第三目标点云数据的第三目标检测结果;所述基于所述第一目标点云数据、以及所述第一目标点云数据的第一目标检测结果,生成样本数据,包括:基于所述第一目标点云数据、所述第一目标点云数据的第一目标检测结果、所述第三目标点云数据、所述第三目标点云数据的第三目标检测结果,生成所述样本数据。
- 根据权利要求6所述的样本生成方法,其特征在于,所述数据增强处理,包括下述至少一种:随机缩放场景处理、随机旋转场景处理、随机沿坐标轴翻转场景处理、随机物体缩放处理、随机物体旋转处理、随机沿坐标轴采样点云处理。
- 一种神经网络的训练方法,其特征在于,包括:利用权利要求1-7任一项所述的样本生成方法生成样本数据;利用所述样本数据,训练待训练的目标检测神经网络,得到训练后的目标检测神经网络。
- 一种数据处理方法,其特征在于,包括:获取待处理的点云数据;利用基于权利要求8所述的神经网络的训练方法训练的神经网络,对所述待处理的点云数据进行目标检测,得到目标检测结果。
- 一种智能行驶装置的行驶控制方法,其特征在于,包括:获取智能行驶装置在行驶过程中采集的点云数据;利用基于权利要求8所述的神经网络的训练方法训练的神经网络,检测所述点云数据中的目标对象;基于检测的目标对象,控制所述智能行驶装置。
- 一种样本生成装置,其特征在于,包括:第一检测模块,用于对多帧第一点云数据中的每帧第一点云数据进行目标检测,得到每帧第一点云数据的第一目标检测结果;确定模块,用于基于所述每帧第一点云数据的第一目标检测结果、表征点云数据中存在目标的第一置信度阈值、以及表征点云数据中不存在目标的第二置信度阈值,从所述多帧第一点云数据中,确定第一目标点云数据;第一生成模块,用于基于所述第一目标点云数据、以及所述第一目标点云数据对应的第一目标检测结果,生成样本数据。
- 一种神经网络的训练装置,其特征在于,包括:第二生成模块,用于利用权利要求1-7任一项所述的样本生成方法生成样本数据;模型训练模块,用于利用所述样本数据,训练待训练的目标检测神经网络,得到训练后的目标检测神经网络。
- 一种数据处理装置,其特征在于,包括:第一获取模块,用于获取待处理的点云数据;处理模块,用于利用基于权利要求8所述的神经网络的训练方法训练的神经网络,对所述待处理的点云数据进行处理,得到所述待处理的点云数据的数据处理结果。
- 一种智能行驶装置的行驶控制装置,其特征在于,包括:第二获取模块,用于获取智能行驶装置在行驶过程中采集的点云数据;第二检测模块,用于利用基于权利要求8所述的神经网络的训练方法训练的神经网络,检测所述点云数据中的目标对象;控制模块,用于基于检测的目标对象,控制所述智能行驶装置。
- 一种计算机设备,其特征在于,包括:处理器、存储器,所述存储器存储有所述处理器可执行的机器可读指令,所述处理器用于执行所述存储器中存储的机器可读指令,所述机器可读指令被所述处理器执行时,所述处理器执行如权利要求1至7任一项所述的样本生成方法的步骤;或者权利要求8所述的神经网络的训练方法的步骤;或者权利要求9所述的数据处理方法的步骤;或者权利要求10所述的智能行驶装置的行驶控制方法的步骤。
- 一种计算机可读存储介质,其特征在于,所述计算机可读存储介质上存储有计算机程序,所述计算机程序被计算机设备运行时,所述计算机设备执行如权利要求1至7任一项所述的样本生成方法的步骤;或者权利要求8所述的神经网络的训练方法的步骤;或者权利要求9所述的数据处理方法的步骤;或者权利要求10所述的智能行驶装置的行驶控制方法的步骤。
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CN112163643A (zh) * | 2020-10-30 | 2021-01-01 | 上海商汤临港智能科技有限公司 | 样本生成、神经网络的训练、数据处理方法及装置 |
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CN115994589A (zh) * | 2023-03-23 | 2023-04-21 | 北京易控智驾科技有限公司 | 训练方法和装置、目标检测方法、电子设备和存储介质 |
CN115994589B (zh) * | 2023-03-23 | 2023-05-23 | 北京易控智驾科技有限公司 | 训练方法和装置、目标检测方法、电子设备和存储介质 |
CN116721399A (zh) * | 2023-07-26 | 2023-09-08 | 之江实验室 | 一种量化感知训练的点云目标检测方法及装置 |
CN116721399B (zh) * | 2023-07-26 | 2023-11-14 | 之江实验室 | 一种量化感知训练的点云目标检测方法及装置 |
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