CN117635924B - Low-energy-consumption target detection method based on adaptive knowledge distillation - Google Patents
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Abstract
The invention discloses a low-energy-consumption target detection method based on self-adaptive knowledge distillation, and aims to solve the problem that resource-limited equipment is easily affected by data drift in the target detection process. Firstly, the terminal equipment carrying the lightweight target detection algorithm periodically uploads the image, the detection result and the residual electric quantity information which are partially detected to the edge server carrying the high-precision target detection model. Then, the edge server detects the uploaded image, calculates the relative loss value of precision, the average image similarity and the energy consumption proportion, finally obtains the self-adaptive sampling rate and the training trigger instruction, and transmits the self-adaptive sampling rate and the training trigger instruction back to the terminal equipment. And finally, the terminal equipment extracts part of images from the experience pool, forms a training set with the received data, performs model training and updates the sampling rate and the experience pool. The invention can solve the problem of detection precision reduction caused by the influence of data drift on the resource-constrained equipment, improve the detection precision and realize continuous learning with low energy consumption.
Description
Technical Field
The invention belongs to the technical field of video target detection in computer vision, and particularly relates to a low-energy-consumption target detection method based on self-adaptive knowledge distillation under a terminal-side collaborative framework, which realizes a low-energy-consumption continuous learning technology of resource-limited equipment.
Background
Thanks to the rapid development of the deep learning technology, the accuracy of the target detection model is significantly improved. The improvement of the accuracy of the object detection model is derived from more accurate extraction of image features and higher-level semantic understanding of the deep learning model. However, as the complexity of the model increases, the model parameters also exhibit a rapidly increasing trend. Large-scale deep networks may involve hundreds of millions or even billions of parameters, which makes deployment of models on resource-constrained devices extremely difficult. The resource-constrained devices, such as embedded systems, mobile devices or edge computing devices, have limited computing, storage capabilities and power, and cannot effectively support large-scale model parameters. This results in that in a resource-limited environment, the object detection model cannot achieve efficient real-time reasoning, or even run on the device.
To address this challenge, researchers have proposed a series of approaches to model weight reduction and optimization. These methods include techniques such as network pruning, parameter quantization (PARAMETER QUANTIZATION), model compression (model compression), etc., in order to reduce the number of parameters of the model. In addition, there are some specially designed lightweight object detection models, such as MobileNet, efficientDet, which, by careful design of structures and strategies, reduce the parameters and computational complexity of the model while maintaining high accuracy. While real-time reasoning can be implemented on resource constrained devices by reducing the number of parameters, the reduction in the number of parameters also means that the model is more sensitive to data drift (DATA DRIFT). When there is a discrepancy between the deployed scene and the data set used in model training, a dramatic decrease in the accuracy of target detection may result.
Knowledge distillation is expected to solve this problem. As one of the continuous learning techniques, a strategy of knowledge distillation is to solve the problem of data drift sensitivity caused by the reduction of the parameter amount by transferring the knowledge of a complex teacher model to a light-weight student model. The basic idea of knowledge distillation is to take the prediction result of the teacher model as an auxiliary target of the student model and learn by minimizing the distance between the prediction distribution of the teacher model and the student model. However, for the terminal device with limited resources, it is not practical to frequently upload and download the model parameters, which causes high time delay and energy consumption.
Therefore, the invention discloses a low-energy-consumption target detection method based on adaptive knowledge distillation. The knowledge distillation technology under the cooperation of the end edges is designed, and the energy consumption of the terminal equipment is reduced by adaptively adjusting the sampling rate and the training trigger instruction.
Disclosure of Invention
The invention aims to solve the problems of the prior art, and provides a low-energy-consumption target detection method based on adaptive knowledge distillation, which solves the problem that resource-limited equipment is easily influenced by data drift in the target detection process by decoupling a training and labeling part of the knowledge distillation and adaptively adjusting a sampling rate and a training trigger instruction, and reduces the energy consumption in the knowledge distillation process.
The technical solution for realizing the purpose of the invention is as follows: a low energy consumption target detection method based on adaptive knowledge distillation, the method comprising the steps of:
Step 1: the terminal equipment carrying the lightweight target detection algorithm selects partial images from the detected images according to the sampling rate, and stores the characteristic images into a buffer pool; meanwhile, uploading the image feature map, the detection result and the current battery energy remaining proportion information in the buffer pool to an edge server carrying a high-precision and high-generalization capacity target detection model;
Step 2: edge server pair uploaded image feature map High-precision detection is carried out to obtain a detection result/>And based on the detection result and the uploaded remaining energy information/>Calculating the relative loss value/>Average image similarityRatio of energy consumption/>Obtaining the adaptive sampling rate/>Training trigger instruction/>; Then, the edge server judges the relative loss value/>And threshold/>If/>Less than/>Then/>High-precision model detection result/>, on edge serverSampling rate at next time/>Training trigger instruction/>Issuing to terminal equipment; if/>Greater than or equal to/>Then/>Only issue training instructions/>And the next time sampling rate/>To the terminal device;
Step 3: the terminal equipment randomly extracts the image feature images with the same number from the experience pool, and forms a training set with the downloaded image feature images and the labels thereof, and is used for training the carried model and updating the sampling rate; after training is completed, the experience pool is updated, and the extracted image feature map is replaced by the downloaded image feature map.
Further, step 1: the terminal equipment carrying the lightweight target detection algorithm selects partial image feature images from the detected images according to the sampling rate and stores the partial image feature images into a buffer pool; meanwhile, the image feature map, the detection result and the current battery energy remaining proportion information in the buffer pool are uploaded to an edge server carrying a high-precision and high-generalization capacity target detection model, and the method specifically comprises the following steps:
Step 1-1: the terminal equipment collects images in real time to detect targets and samples the images according to the sampling rate Randomly sampling from the detected image, and storing the characteristic diagram and the detection result into a buffer pool;
Step 1-2: every other time slot The terminal equipment will buffer all image feature maps/>Corresponding target detection result/>Constitute feature map set/>Sum/>Tense and aggregate the feature map/>Device battery current remaining energy information/>Uploading to an edge server;
Further, step 2: the purpose of self-adaptive adjustment of the sampling rate is to measure the training requirement on the terminal equipment according to the current system state, and when the requirement is lower, the sampling rate is reduced, so that the energy consumption of transmission and training can be reduced; when the demand is higher, increase sampling rate, can increase training sample, improve the precision fast. If the self-adaptive method is not adopted, the sampling rate cannot be reduced when the training requirement is low, so that resource waste is generated, and the sampling rate cannot be quickly improved when the requirement is high, so that the accuracy cannot be quickly improved. Edge server pair uploaded image feature map High-precision detection is carried out to obtain a detection result/>And based on the detection result and the uploaded remaining energy information/>Calculating the relative loss value/>Average image similarity/>Ratio of energy consumption/>Obtaining the adaptive sampling rate/>Training trigger instruction/>; Then, the edge server judges the relative loss value/>And threshold/>If/>Less than/>Then/>Detecting results of high-precision models on edge serversSampling rate at next time/>Training trigger instruction/>Issuing to terminal equipment; if/>Greater than or equal toThen/>Only issue training instructions/>And the next time sampling rate/>To the terminal device; the method specifically comprises the following steps:
step 2-1: the edge server uploads the image feature map Inputting into high-precision detection network to obtain detection result；
Step 2-2: the detection result is processedAs a real tag, calculating the detection result/>, of the terminal equipmentMAP value of each image in the image is taken as the calculated accuracy relative loss value/>, and then the average value is taken as the calculated accuracy relative loss value/>; Calculate feature map set/>Middle/>Average similarity/>, of feature images of a sheet of imagesThe calculation mode is as follows:，
Wherein, Is the number of uploaded images,/>、/>And/>Is constant,/>And/>Feature map set/>, respectivelyMedium feature map/>Mean and variance of output vector obtained after inputting target detection model on edge server,/>Is in the range of; Calculation/>Ratio of energy consumption to battery remaining energy required for image transmission and training on end devices/>The calculation mode is as follows:
，
Wherein, Is the sampling rate,/>Is the slot length,/>Is the current remaining energy information of the battery of the device,/>Bandwidth allocated to an edge server to a terminal device,/>For the transmission power of the terminal device,/>For the channel gain,/>Is channel noise,/>Data volume for one image,/>The energy consumption required for locally training an image;
step 2-3: calculating an adaptive sampling rate The calculation mode is as follows:，
Wherein, The accuracy relative loss value calculated in the step 2-2, the average similarity of the image characteristic images, the ratio of the required energy to the residual energy, and the ratio of the required energy to the residual energy are respectively calculated in the step 2-2、/>And/>Respectively are parameters/>、/>And/>The accuracy relative loss threshold value, the feature map similarity threshold value and the energy ratio threshold value are respectively/>And/>For the lower and upper bounds of the sampling rate,/>Indicating that the calculation result is not out of limits; calculate training trigger/>The calculation mode is as follows:
，
Step 2-4: according to training trigger instruction, the edge server Judging the data needing to be returned according to the calculation result, ifThe high-precision model detection result/>, on the edge server、/>Time sampling rate/>Training trigger instruction/>Issuing to terminal equipment; if/>Issuing training trigger instruction/>And/>Time of day sampling rate；
Further, step 3: the terminal equipment randomly extracts the image feature images with the same number from the experience pool, and forms a training set with the downloaded image feature images and the labels thereof, and is used for training the carried model and updating the sampling rate; after training is completed, updating the experience pool, and replacing the extracted image feature map with the downloaded image feature map; the method specifically comprises the following steps:
Step 3-1: the terminal equipment receives the data returned from the edge server; if training trigger instruction The device updates the sampling rate to/>; If training trigger instruction/>Performing the training operation of the step 3-2;
Step 3-2: if training trigger instruction Randomly extracting the quantity from the experience pool as/>And the feature map set saved in the last time slot/>And detection results/>, downloaded from an edge serverForm training setThen training a target detection model on the terminal equipment; after training is completed, the feature map is assembled/>Detection result/>Replacing the extracted feature map and storing the feature map into an experience pool; device update sampling rate is/>; After completion, steps 1-1 to 3-2 are repeated.
Compared with the prior art, the invention has the remarkable advantages that: 1) The target detection method is based on a knowledge distillation technology, solves the problem that the resource-limited equipment is easily affected by data drift in the target detection process, and effectively improves the target detection precision; 2) The training and labeling parts in the knowledge distillation frame with the coordinated end edges are decoupled and respectively deployed on the end equipment and the edge server, and the self-adaptive sampling rate and the training trigger instruction are designed, so that the continuous learning process with low energy consumption can be realized.
The invention is described in further detail below with reference to the accompanying drawings.
Drawings
FIG. 1 shows a schematic overall framework of a low energy consumption target detection method based on adaptive knowledge distillation of the present invention.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
It should be noted that, if there is a description of "first", "second", etc. in the embodiments of the present invention, the description of "first", "second", etc. is only for descriptive purposes, and is not to be construed as indicating or implying relative importance or implying that the number of technical features indicated is indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions of the embodiments may be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the scope of protection claimed in the present invention.
The invention aims to solve the problems of the prior art, and provides a low-energy-consumption target detection method based on adaptive knowledge distillation, which solves the problem that resource-limited equipment is easily influenced by data drift in the target detection process by decoupling a training and labeling part of the knowledge distillation and adaptively adjusting a sampling rate and a training trigger instruction, and reduces the energy consumption in the knowledge distillation process.
FIG. 1 shows a schematic overall framework of a low energy consumption target detection method based on adaptive knowledge distillation of the present invention. Referring to fig. 1, the implementation steps of the method of the present embodiment are described in detail below.
Step 1-1: the terminal equipment is provided with a lightweight target detection model MobileNet, acquires images in real time through a camera to detect targets, and performs target detection according to the sampling rateRandomly sampling from the detected image, and storing the characteristic diagram and the detection result into a buffer pool;
Step 1-2: every other time slot The terminal equipment will buffer all image feature maps/>Corresponding target detection result/>Constitute feature map set/>Sum/>Tense and aggregate the feature map/>Device battery current remaining energy information/>Uploading to an edge server;
step 2-1: the edge server uploads the image feature map Inputting a high-precision detection network Fast-R-CNN to obtain a detection result/>；
Step 2-2: the detection result is processedAs a real tag, calculating the detection result/>, of the terminal equipmentMAP value of each image in the image is taken as the calculated accuracy relative loss value/>, and then the average value is taken as the calculated accuracy relative loss value/>; Calculate feature map set/>Middle/>Average similarity/>, of feature images of a sheet of imagesThe calculation mode is as follows:，
Wherein, Is the number of uploaded images,/>、/>And/>Is constant,/>And/>Feature map set/>, respectivelyMedium feature map/>Mean and variance of output vector obtained after inputting target detection model on edge server,/>Is in the range of; Calculation/>Ratio of energy consumption to battery remaining energy required for image transmission and training on end devices/>The calculation mode is as follows:
，
Wherein, Is the sampling rate,/>Is the slot length,/>Is the current remaining energy information of the battery of the device,/>Bandwidth allocated to an edge server to a terminal device,/>For the transmission power of the terminal device,/>For the channel gain,/>Is channel noise,/>Data volume for one image/>The energy consumption required for locally training an image;
step 2-3: calculating an adaptive sampling rate The calculation mode is as follows:
，
Wherein, among them, The accuracy relative loss value calculated in the step 2-2, the average similarity of the image characteristic images, the ratio of the required energy to the residual energy, and the ratio of the required energy to the residual energy are respectively calculated in the step 2-2、/>And/>Respectively are parameters/>、And/>The accuracy relative loss threshold value, the feature map similarity threshold value and the energy ratio threshold value are respectively/>And/>For the lower and upper bounds of the sampling rate,/>Indicating that the calculation result is not out of limits; calculate training trigger/>The calculation mode is as follows:
，
Step 2-4: according to training trigger instruction, the edge server Judging the data needing to be returned according to the calculation result, ifThe high-precision model detection result/>, on the edge server、/>Time sampling rate/>Training trigger instruction/>Issuing to terminal equipment; if/>Issuing training trigger instruction/>And/>Time of day sampling rate；
Step 3-1: the terminal device receives the data transmitted back from the edge server. If training trigger instructionThe device updates the sampling rate to/>; If training trigger instruction/>Performing the training operation of the step 3-2;
Step 3-2: if training trigger instruction Randomly extracting the quantity from the experience pool as/>And the feature map set saved in the last time slot/>And detection results/>, downloaded from an edge serverForm training setAnd then training the target detection model on the terminal equipment. After training is completed, the feature map is assembled/>Detection result/>Replacing the extracted feature map and storing the feature map into an experience pool; device update sampling rate is/>Then enter the next time slot, repeat step 1-1 to step 3-2.
The foregoing has outlined and described the basic principles, features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, and that the above embodiments and descriptions are merely illustrative of the principles of the present invention, and various changes and modifications may be made without departing from the spirit and scope of the invention, which is defined in the appended claims. The scope of the invention is defined by the appended claims and equivalents thereof.
Claims (4)
1. A low energy consumption target detection method based on adaptive knowledge distillation, characterized in that the method comprises the following steps:
Step 1: the terminal equipment carrying the lightweight target detection algorithm selects partial images from the detected images according to the sampling rate, and stores the characteristic images into a buffer pool; meanwhile, uploading the image feature map, the detection result and the current battery energy remaining proportion information in the buffer pool to an edge server carrying a high-precision and high-generalization capacity target detection model;
Step 2: edge server pair uploaded image feature map High-precision detection is carried out to obtain a detection result/>And based on the detection result and the uploaded remaining energy information/>Calculating the relative loss value/>Average image similarity/>Ratio of energy consumption/>Obtaining the adaptive sampling rate/>Training trigger instruction/>; Then, the edge server judges the relative loss value/>And threshold/>If/>Less than/>Then/>High-precision model detection result/>, on edge serverSampling rate at next time/>Training trigger instruction/>Issuing to terminal equipment; if/>Greater than or equal to/>Then/>Only issue training instructions/>And the next time sampling rate/>To the terminal device;
Step 3: the terminal equipment randomly extracts the image feature images with the same number from the experience pool, and forms a training set with the downloaded image feature images and the labels thereof, and is used for training the carried model and updating the sampling rate; after training is completed, the experience pool is updated, and the extracted image feature map is replaced by the downloaded image feature map.
2. The method for detecting the low-energy-consumption target based on the adaptive knowledge distillation according to claim 1, wherein in step 1, the terminal device carrying the lightweight target detection algorithm selects a part of image feature images from the detected images according to a sampling rate, and stores the part of image feature images in a buffer pool; meanwhile, the image feature map, the detection result and the current battery energy remaining proportion information in the buffer pool are uploaded to an edge server carrying a high-precision and high-generalization capacity target detection model, and the method specifically comprises the following steps:
Step 1-1: the terminal equipment collects images in real time to detect targets and samples the images according to the sampling rate Randomly sampling from the detected image, and storing the characteristic diagram and the detection result into a buffer pool;
Step 1-2: every other time slot The terminal equipment will buffer all image feature maps/>Corresponding target detection result/>Constitute feature map set/>Sum/>Tense and aggregate the feature map/>Device battery current remaining energy information/>Uploading to an edge server.
3. The method for detecting a target with low energy consumption based on adaptive knowledge distillation according to claim 1, wherein in step2, the edge server performs image feature map uploadingHigh-precision detection is carried out to obtain a detection result/>And based on the detection result and the uploaded remaining energy information/>Calculating the relative loss value/>Average image similarity/>Ratio of energy consumption/>Obtaining the adaptive sampling rate/>Training trigger instruction/>; Then, the edge server judges the relative loss value/>And threshold/>If/>Less than/>Then/>High-precision model detection result/>, on edge serverSampling rate at next time/>Training trigger instruction/>Issuing to terminal equipment; if it isGreater than or equal to/>Then/>Only issue training instructions/>And the next time sampling rate/>To the terminal device; the method specifically comprises the following steps:
step 2-1: the edge server uploads the image feature map Inputting into high-precision detection network to obtain detection result；
Step 2-2: the detection result is processedAs a real tag, calculating the detection result/>, of the terminal equipmentMAP value of each image in the image is taken as the calculated accuracy relative loss value/>, and then the average value is taken as the calculated accuracy relative loss value/>; Calculate feature map set/>Middle/>Average similarity/>, of feature images of a sheet of imagesThe calculation mode is as follows:，
Wherein, Is the number of uploaded images,/>、/>And/>Is constant,/>And/>Feature map set/>, respectivelyMedium feature map/>Mean and variance of output vector obtained after inputting target detection model on edge server,/>Is in the range of/>; Calculation/>Ratio of energy consumption to battery remaining energy required for image transmission and training on end devices/>The calculation mode is as follows:，
Wherein, Is the sampling rate,/>Is the slot length,/>Is the current remaining energy information of the battery of the device,/>Bandwidth allocated to an edge server to a terminal device,/>For the transmission power of the terminal device,/>For the channel gain,/>In the event of channel noise,Data volume for one image,/>The energy consumption required for locally training an image;
step 2-3: calculating an adaptive sampling rate The calculation mode is as follows:
，
Wherein, The accuracy relative loss value calculated in the step 2-2, the average similarity of the image characteristic images, the ratio of the required energy to the residual energy, and the ratio of the required energy to the residual energy are respectively calculated in the step 2-2、/>And/>Respectively are parameters/>、/>AndThe accuracy relative loss threshold value, the feature map similarity threshold value and the energy ratio threshold value are respectively/>And/>For the lower and upper bounds of the sampling rate,/>Indicating that the calculation result is not out of limits; calculate training trigger/>The calculation mode is as follows:
，
Step 2-4: according to training trigger instruction, the edge server Judging the data needing to be returned according to the calculation result, ifThe high-precision model detection result/>, on the edge server、/>Time sampling rate/>Training trigger instruction/>Issuing to terminal equipment; if/>Only send out training trigger command/>And/>Time sampling rate/>。
4. The method for detecting the low-energy-consumption target based on the adaptive knowledge distillation according to claim 1, wherein in the step 3, the terminal equipment randomly extracts the same number of image feature images from the experience pool, and forms a training set with the downloaded image feature images and labels thereof, so as to train the carried model and update the sampling rate; after training is completed, updating the experience pool, and replacing the extracted image feature map with the downloaded image feature map; the method specifically comprises the following steps:
Step 3-1: the terminal equipment receives the data returned from the edge server; if training trigger instruction The device updates the sampling rate to/>; If training trigger instruction/>Performing the training operation of the step 3-2;
Step 3-2: if training trigger instruction Randomly extracting the quantity from the experience pool as/>And the feature map set saved in the last time slot/>And detection results/>, downloaded from an edge serverConstitute training set/>Then training a target detection model on the terminal equipment; after training is completed, the feature map is assembled/>Detection resultReplacing the extracted feature map and storing the feature map into an experience pool; device update sampling rate is/>; After completion, steps 1-1 to 3-2 are repeated.
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| CN110096362A (en) * | 2019-04-24 | 2019-08-06 | 重庆邮电大学 | A kind of multitask discharging method based on Edge Server cooperation |
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