CN109977812A - A kind of Vehicular video object detection method based on deep learning - Google Patents

A kind of Vehicular video object detection method based on deep learning Download PDF

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CN109977812A
CN109977812A CN201910185300.4A CN201910185300A CN109977812A CN 109977812 A CN109977812 A CN 109977812A CN 201910185300 A CN201910185300 A CN 201910185300A CN 109977812 A CN109977812 A CN 109977812A
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CN109977812B (en
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张登银
金天宇
丁飞
赵莎莎
刘锦
薛睿
聂涵
王雪纯
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Nanjing Post and Telecommunication University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • 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
    • G06V10/443—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 by matching or filtering
    • G06V10/449—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters
    • G06V10/451—Biologically inspired filters, e.g. difference of Gaussians [DoG] or Gabor filters with interaction between the filter responses, e.g. cortical complex cells
    • G06V10/454—Integrating the filters into a hierarchical structure, e.g. convolutional neural networks [CNN]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle

Abstract

The invention discloses a kind of Vehicular video object detection method based on deep learning realizes the target detection in complicated traffic environment using improved Faster R-CNN algorithm, provides traffic safety miscellaneous function.There is serious Small object missing inspection in existing target tracking algorism, the present invention is by increasing a depth information channel, it is in parallel with original color image channel, and it is merged on channel dimension, candidate frame extraction and target detection are carried out on fused characteristic image, the verification and measurement ratio of Small object is improved, in addition training of the addition to difficult sample in training improves the object recognition rate of algorithm entirety.The present invention can fully consider Small object missing inspection problem existing for Faster R-CNN algorithm, by depth image Fusion Features and difficult sample method for digging, improve the accuracy rate of vehicle identification in vehicles in complex traffic scene.

Description

A kind of Vehicular video object detection method based on deep learning
Technical field
The present invention relates to a kind of Vehicular video object detection method based on deep learning, belongs to video image processing technology Field.
Background technique
When driving, to the vehicle of vehicle front, pedestrian and other barriers progress object detecting and tracking, and The behavioural analysis that front truck is carried out on the basis of this, is the basis of Driving assistant system.The main step of conventional target detection method It is rapid general are as follows: extraction target signature, the corresponding classifier of training, sliding window search repeat and wrong report filtered.This target inspection The sliding window selection strategy of survey does not have specific aim, and time complexity is high, window redundancy, and the feature robustness of hand-designed is poor, Classifier is unreliable；Simultaneously existing algorithm of target detection can not neatly training data it is effective to be learnt according to different demands Feature complete specific Detection task.
Summary of the invention
It is an object of the invention to solve the above the deficiencies in the prior art, a kind of Vehicular video based on deep learning is provided Object detection method.
In order to achieve the above objectives, the present invention adopts the following technical scheme that:
A kind of Vehicular video object detection method based on deep learning, includes the following steps:
Step 1) snaps to the pixel under depth coordinate under color coordinate；Depth image and color image are respectively led to again It crosses CNN and carries out feature extraction, and the characteristic pattern that respective convolutional layer exports is subjected to fused in tandem on channel dimension and is obtained finally RGB-D feature as the convolution Feature Mapping after convolution；
Step 2) constructs region and suggests network RPN, and the region suggests that network RPN includes one 3 × 3 convolutional layer and two A 1 × 1 parallel-convolution layer；By the convolutional layer of fused convolution Feature Mapping input 3 × 3, in the Feature Mapping of input The network of default size is slided as unit of pixel, then each sliding position generates the anchor point of particular dimensions；
The parallel-convolution layer that the anchor point of generation inputs two 1 × 1 is subjected to position recurrence and the judgement of front and back scape, is exported respectively Before the front and back scape confidence level of anchor point and all candidate frame positions and selecting after resulting rectangle according to preset condition filters out in frame The highest preceding certain amount of region of scape confidence level, obtains final region set of suggestions C；
Fast R-CNN model is constructed, the Fast R-CNN model is by two pond ROI layers, a full articulamentum and two The full articulamentum of a parallel connection forms, the confidence level for exporting the region respectively and the candidate frame position after frame recurrence；It will melt Convolution feature after conjunction inputs Fast R-CNN model, exports the position of target and its classification and confidence level in image；
Step 3): the cost function of training RPN network and the cost function of training Fast R-CNN network are constructed；
Step 4) passes through the standard variance from setting using the ZF model training of standard and the parameters of trim network Weight, which is extracted, in zero-mean gaussian distribution carrys out all mew layers of random initializtion；
Step 5) utilizes back-propagation algorithm and stochastic gradient descent algorithm, using to two nets of RPN and Fast R-CNN The mode of network alternating training is trained model, and the weight of every layer of neural network is sequentially adjusted according to pre-set parameter；
Step 6) the Faster R-CNN model good using the training set test initial training being obtained ahead of time, according to difficult sample Discrimination formula filter out difficult sample；
The difficult sample generated in step 6) is added in training set step 7), is trained again to network, repeats step 5)-step 7) finally obtains optimal Faster R-CNN model；
Step 8) handles the Vehicular video image acquired in practice, inputs trained Faster R-CNN model In, export target category, confidence level and target position in the image.
Advantageous effects of the invention:
First, the present invention proposes a kind of based on depth information benefit on the basis of the convolutional neural networks model based on suggestion Full target detection model, improved Faster R-CNN is added to a depth information channel, by color image and depth map CNN as passing through structure respectively carries out feature extraction, and two CNN are using structure in parallel, then by original color image Feature Mapping and depth image Feature Mapping carry out fused in tandem, obtain final characteristics of image, compared with original algorithm, this hair Bright obtained characteristics of image is more abundant, the detailed information supplemented with vehicle, and not will increase time overhead, meets and improves again The requirement of target detection under miscellaneous scene.
Second, the present invention is by increasing difficult sample Mining Strategy in the training stage, so that model is on the original basis more Difficult sample is paid close attention to, the background of vehicle and doubtful vehicle is preferably distinguished, achievees the purpose that improve accuracy；
Third, the present invention extract the Faster R-CNN algorithm of suggestion candidate frame in real-time using shared convolutional network On have been significantly improved, which has abandoned traditional region proposed algorithm, is mentioned using the convolutional layer in depth network Candidate frame is taken, plenty of time expense has been saved.
Detailed description of the invention
Fig. 1 is the flow diagram of specific embodiment of the invention method.
Fig. 2 is the training flow chart of improved Faster R-CN algorithm in the specific embodiment of the invention.
Specific embodiment
The invention will be further described below in conjunction with the accompanying drawings.Following embodiment is only used for clearly illustrating the present invention Technical solution, and not intended to limit the protection scope of the present invention.
Present invention aims at a kind of Vehicular video object detection method based on deep learning is proposed, in Faster On the basis of R-CNN, the Feature Mapping of depth image is added to supplement Vehicle Detail information, selects and extracts color image feature Identical convolutional neural networks, color image channel use structure in parallel with depth image channel, and the feature extracted is passed through Fused in tandem obtains final RGB-D feature, and difficult sample Mining Strategy is added in training, improves algorithm in complicated traffic field To the detection accuracy of Small object and difficult target in scape.
It is as shown in Figure 1 the method flow diagram of the specific embodiment of the invention.
Can be based on the training set sample set and test set sample obtained in advance when implementing the method for the present invention, it can also To make training set and test set according to demand.In the present embodiment, training sample set and test are constructed using KITTI data set Sample set includes the following steps 1: using the format and evaluation algorithms tool of PASCAL VOC data set.Firstly, conversion KITTI Classification: a total of 20 classifications of PASCAL VOC, in urban transportation scene, emphasis test object be vehicle, pedestrian, traffic Mark, therefore data set is divided into above-mentioned 3 kinds of classifications；Secondly, conversion markup information: xml is converted from txt by mark file, Remove the other information in mark, leaves behind 3 classes；Finally, generating training verifying collection and test set.
As described in Figure 1, the method for the present invention includes the following steps:
Step 2) constructs a kind of improved Faster R-CNN model, which combines region and suggest network (Regional Proposal Network, RPN) and Fast R-CNN network；
2.1) convolutional neural networks (Convolutional Neural Networks, CNN)
Firstly, the pixel under depth coordinate is snapped under color coordinate；CNN chooses the net of feature extraction in ZF model Network, the identical CNN of two structures is carried out parallel connection, and (original color image channel is channel 1, and depth image channel in parallel is Channel 2)；After two class images pass through CNN feature extraction, characteristic pattern size is that (h, w respectively indicate characteristic pattern to hwc here High, wide, c is tri- channels RGB), Fusion Features are carried out using color image feature and depth image feature as two channels, are melted Characteristic pattern size after conjunction is 2hwc；
2.2) region suggests that network RPN includes one 3 × 3 convolutional layer and two 1 × 1 parallel-convolution layers；
By the convolutional layer of fused convolution Feature Mapping input 3 × 3, as unit of in the Feature Mapping of input by pixel A small network is slided, 3 scales and 3 length-width ratios are respectively adopted in the present embodiment, then each sliding position generates k=3 × 3 The anchor point of=9 different scales then generates hwk anchor point in total, obtains hwk rectangle candidate frame；
Two 1 × 1 parallel-convolution layers carry out position recurrence to upper one layer of anchor point and front and back scape judges, export anchor respectively The front and back scape confidence level of point and candidate frame position.Candidate frame position includes candidate frame center point coordinate x and y, and width w ' and height H ' totally four parameters；
2.2) 2.1) resulting rectangle candidate frame is filtered out to the predetermined quantity for meeting preset condition according to preset condition Region.Descending sort is carried out according to the score of softmax to resulting rectangle candidate frame in the present embodiment, retains preceding 2000 areas Domain, further with non-maxima suppression algorithm (Non-Maximum Suppression, NMS), the prospect confidence level of filtering out is most High preceding 300 regions, obtain final region set of suggestions C；
2.3) Fast R-CNN is made of two pond ROI layers, a full articulamentum and two full articulamentums in parallel, point The confidence level for not exporting the region and the candidate frame position after frame recurrence；
Pooling layers of ROI carry out pondization operation, ROI to region set of suggestions C and fused convolution Feature Mapping ROI is mapped to the corresponding position of Feature Mapping by the pooling layers of image according to input, is phase by the region division after mapping With the sections of size, maximum pondization operation is carried out to each section；
Full articulamentum merges pooling layers of ROI of output result, recently enters two full articulamentums in parallel, Territorial classification is carried out to candidate frame and frame returns, exports the position of target and its classification, confidence level in image；
Step 3) constructs the cost function of training RPN network and the cost function of training Fast R-CNN network:
The cost function of training RPN network in the present embodiment are as follows:
Wherein, the friendship of anchor point and ground truth (i.e. calibrated truthful data) and than (Intersection over Union, IoU) it is maximum or be designated as positive sample, P not less than 0.7iFor forecast confidence；For mark value, positive sample is indicated when taking 1 This, indicates negative sample when taking 0；The index of i expression anchor point；NclsFor anchor point total quantity；NregFor the quantity of positive sample；tiFor prediction Anchor point bounding box correction value；For actual anchor point bounding box correction value；LclsFor cost of classifying；LregGeneration is returned for frame Valence；λ is balance weight；
The cost function of training Fast R-CNN network in the present embodiment are as follows:
L(p,u,tu, v) and=Lcls(p,u)+λ[u≥1]Lreg(tu,v)
Wherein, u is u class；tuFor the correction value of u class frame regression forecasting；V is actual correction value；LclsFor generation of classifying Valence；LregCost is returned for frame；λ is balance weight；
Step 4) passes through the standard variance from setting using the ZF model training of standard and the parameters of trim network Weight, which is extracted, in zero-mean gaussian distribution carrys out all mew layers of random initializtion；
Step 5) utilizes back-propagation algorithm and stochastic gradient descent algorithm, using to two nets of RPN and Fast R-CNN The mode of network alternating training is trained model, is sequentially adjusted in the weight of every layer of neural network, the initial learning rate of network is set as 0.01, minimum learning rate is set as 0.0001, and momentum is set as 0.9, and weight attenuation coefficient is that 0.0005, Dropout value is set as 0.5, the specific steps are as follows:
(1) using back-propagation algorithm and stochastic gradient algorithm's training RPN model, the stage iteration 80000 times；
(2) input of the candidate frame for using RPN to generate as Fast R-CNN, and stand-alone training is carried out, the stage iteration 40000 times；
(3) RPN network structure is initialized using the result in (2), fixed shared convolutional layer is (by shared convolutional layer Learning rate be set as 0), update RPN network parameter, the stage iteration 80000 times；
(4) fixed shared convolutional layer (setting 0 for the learning rate of shared convolutional layer), finely tunes Fast R-CNN network knot Structure updates the parameter of its full articulamentum, the stage iteration 40000 times；
Step 6) the Faster R-CNN model good using training set test initial training, hardly possible sample according to the present invention are sentenced Other formula filters out difficult sample；
The difficult sample generated in step 6) is added in training set step 7), is trained again to network, repeats step 5), so that Strengthens network finally obtains optimal Faster R-CNN model to the discriminating power of difficult sample；Training process can join See Fig. 2.
Step 8) handles the Vehicular video image acquired in practice, inputs trained Faster R-CNN model In, export target category, confidence level and target position in the image.
The present invention can fully consider Small object missing inspection problem existing for Faster R-CNN algorithm, pass through depth image spy Sign fusion and difficult sample method for digging, improve the accuracy rate of vehicle identification in vehicles in complex traffic scene.
The algorithm of target detection based on convolutional neural networks that the present invention uses can be the flexible training data the case where Under, learn effective feature according to different demands to complete specific Detection task.R-CNN algorithm be based on candidate frame suggestion with A kind of algorithm of target detection that convolutional neural networks combine, since the large number of suggestion that region proposed algorithm generates is candidate Frame and biggish time overhead, the algorithm still have biggish room for promotion in terms of real-time and accuracy.Utilize shared volume Product network has been significantly improved in real-time to extract the Faster R-CNN algorithm of suggestion candidate frame, which abandons Traditional region proposed algorithm, extracts candidate frame using the convolutional layer in depth network, has saved plenty of time expense, but In the more and more complex scene of Small object, the case where missing inspection, is more serious, still there is biggish room for improvement.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, without departing from the technical principles of the invention, several improvement and deformations can also be made, these improvement and deformations Also it should be regarded as protection scope of the present invention.

Claims (10)

1. a kind of Vehicular video object detection method based on deep learning, characterized in that include the following steps:
Step 1) snaps to the pixel under depth coordinate under color coordinate；Again by depth image and color image each by CNN carry out feature extraction, and by the characteristic pattern that respective convolutional layer exports carried out on channel dimension fused in tandem obtain it is final RGB-D feature is as the convolution Feature Mapping after convolution；
Construct region and suggest network RPN, the region suggest network RPN include one 3 × 3 convolutional layer and two 1 × 1 and Row convolutional layer；By the convolutional layer of fused convolution Feature Mapping input 3 × 3, with pixel for singly in the Feature Mapping of input The network of default size is slided in position, then each sliding position generates the anchor point of particular dimensions；
The parallel-convolution layer that the anchor point of generation inputs two 1 × 1 is subjected to position recurrence and the judgement of front and back scape, exports anchor point respectively Front and back scape confidence level and all candidate frame positions and to select in frame screening to meet after resulting rectangle according to preset condition specific The region of the preset quantity of condition obtains final region set of suggestions C；
Step 2) constructs Fast R-CNN model:
The Fast R-CNN model is made of two pond ROI layers, a full articulamentum and two full articulamentums in parallel, point The confidence level for not exporting the region and the candidate frame position after frame recurrence；Fused convolution feature is inputted into Fast R-CNN model exports the position of target and its classification and confidence level in image；
Step 3): the cost function of training RPN network and the cost function of training Fast R-CNN network are constructed；
Step 4) is equal by zero of the standard variance from setting using the ZF model training of standard and the parameters of trim network Weight is extracted in value Gaussian Profile carrys out all mew layers of random initializtion；
Step 5) utilizes back-propagation algorithm and stochastic gradient descent algorithm, hands over using to two networks of RPN and Fast R-CNN Model is trained for trained mode, the weight of every layer of neural network is sequentially adjusted according to pre-set parameter；
Step 6) the Faster R-CNN model good using the training set test initial training being obtained ahead of time, according to sentencing for difficult sample Other formula filters out difficult sample；
The difficult sample generated in step 6) is added in training set step 7), is trained again to network, repeats step 5)-step It is rapid 7), obtain optimal Faster R-CNN model；
Step 8) handles the Vehicular video image acquired in practice, inputs in trained Faster R-CNN model, Export target category, confidence level and target position in the image.
2. a kind of Vehicular video object detection method based on deep learning according to claim 1, characterized in that described The convolution Feature Mapping that RGB-D feature described in step 2) is shared as RPN and Fast R-CNN, matrix form are as follows:
Wherein, i, j, k are intermediate variable, i~[0, h-1], j~[0, w-1 [, k~[0,2c-1], h is characterized the height of figure, and w is The width of characteristic pattern, c are tri- channels RGB；YRGB(i, j, k) is the characteristics of image after Tandem；Ydepth(i, j, k-c) is color Color characteristics of image；Ymerge(i, j, k) is depth image feature.
3. a kind of Vehicular video object detection method based on deep learning according to claim 1, characterized in that described The cost function of training RPN network are as follows:
Wherein, it is designated as positive sample by the friendship with calibrated truthful data and than maximum or not less than 0.7 anchor point, PiFor prediction Confidence level；For mark value, positive sample is indicated when taking 1, indicates negative sample when taking 0；The index of i expression anchor point；NclsFor anchor point Total quantity；NregFor the quantity of positive sample；tiFor the anchor point bounding box correction value of prediction；For the amendment of actual anchor point bounding box Value；LclsFor cost of classifying；LregCost is returned for frame；λ is balance weight.
4. a kind of Vehicular video object detection method based on deep learning according to claim 1, characterized in that described The cost function of training Fast R-CNN network are as follows:
L(p,u,tu, v) and=Lcls(p,u)+λ[u≥1]Lreg(tu,v)
Wherein, u is u class；tuFor the correction value of u class frame regression forecasting；V is actual correction value；LregGeneration is returned for frame Valence, p are classification prediction results.
5. a kind of Vehicular video object detection method based on deep learning according to claim 1, characterized in that described In step 6), the hardly possible sample discriminant function is as follows:
L (o, p)=LIoU(o)+Lscore(p),
Lscore(p)=(1-p),
Wherein, LIoUFor frame error；LscoreFor error in classification；O is the intersection rate of sample and target；K is to be to the sensitivity of threshold value Number；The value range of o and p is 0~1.
6. a kind of Vehicular video object detection method based on deep learning according to claim 1, characterized in that step 5) specific step is as follows:
(1) using back-propagation algorithm and stochastic gradient algorithm's training RPN model, the stage iteration 80000 times；
(2) input of the candidate frame for using RPN to generate as Fast R-CNN, and stand-alone training is carried out, the stage iteration 40000 It is secondary；
(3) RPN network structure is initialized using the result in (2), fixed shared convolutional layer is (by shared convolutional layer Habit rate is set as 0), updating the parameter of RPN network, the stage iteration 80000 times；
(4) fixed shared convolutional layer (setting 0 for the learning rate of shared convolutional layer), finely tunes Fast R-CNN network structure, more The parameter of its new full articulamentum, the stage iteration 40000 times.
7. a kind of Vehicular video object detection method based on deep learning according to claim 1, characterized in that step 5) parameter setting includes that the initial learning rate of network is set as 0.01 in, and minimum learning rate is set as 0.0001, and momentum is set as 0.9, power Weight attenuation coefficient is that 0.0005, Dropout value is set as 0.5.
8. a kind of Vehicular video object detection method based on deep learning according to claim 1, characterized in that in advance The method for obtaining training set and test set includes the following steps:
Training sample set and test sample collection are constructed using KITTI data set；
According to the classification of PASCAL VOC format conversion KITTI, KITTI data set is divided into 3 type of vehicle, pedestrian and traffic Not；
Conversion markup information: xml is converted from txt by mark file, removes the other information in mark, leaves behind 3 classes；Most Afterwards, training verifying collection and test set are generated.
9. a kind of Vehicular video object detection method based on deep learning according to claim 1, characterized in that screening The method for meeting the region of the preset quantity of specified conditions out is as follows:
Resulting rectangle candidate frame is subjected to descending sort according to the score of softmax, retains preceding 2000 regions, further The highest preceding certain amount of region of prospect confidence level is filtered out with non-maxima suppression algorithm.
10. a kind of Vehicular video object detection method based on deep learning according to claim 1, characterized in that step Rapid 4) the described standard variance set is 0.01.
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CN113128564A (en) * 2021-03-23 2021-07-16 武汉泰沃滋信息技术有限公司 Typical target detection method and system based on deep learning under complex background
CN113159198A (en) * 2021-04-27 2021-07-23 上海芯物科技有限公司 Target detection method, device, equipment and storage medium
CN113177133A (en) * 2021-04-23 2021-07-27 深圳依时货拉拉科技有限公司 Image retrieval method, device, equipment and storage medium
CN113173502A (en) * 2021-01-15 2021-07-27 福建电子口岸股份有限公司 Anti-collision method and system based on laser visual fusion and deep learning
CN113240611A (en) * 2021-05-28 2021-08-10 中建材信息技术股份有限公司 Foreign matter detection method based on picture sequence
CN113569702A (en) * 2021-07-23 2021-10-29 闽江学院 Deep learning-based truck single-tire and double-tire identification method
CN113591668A (en) * 2021-07-26 2021-11-02 南京大学 Wide-area unknown dam automatic detection method using deep learning and spatial analysis
CN114220053A (en) * 2021-12-15 2022-03-22 北京建筑大学 Unmanned aerial vehicle video vehicle retrieval method based on vehicle feature matching
WO2022205329A1 (en) * 2021-04-01 2022-10-06 京东方科技集团股份有限公司 Object detection method, object detection apparatus, and object detection system
CN115529475A (en) * 2021-12-29 2022-12-27 北京智美互联科技有限公司 Method and system for detecting video flow content and controlling wind

Families Citing this family (196)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112183274A (en) * 2020-09-21 2021-01-05 深圳中兴网信科技有限公司 Mud car detection method and computer-readable storage medium
CN112132804B (en) * 2020-09-22 2023-10-31 苏州巨能图像检测技术有限公司 Anti-lifting detection method for hub of hub card
CN112132042B (en) * 2020-09-24 2023-08-01 西安电子科技大学 SAR image target detection method based on contrast domain adaptation
CN112233175B (en) * 2020-09-24 2023-10-24 西安交通大学 Chip positioning method and integrated positioning platform based on YOLOv3-tiny algorithm
CN112164017B (en) * 2020-09-27 2023-11-17 中国兵器工业集团第二一四研究所苏州研发中心 Polarization colorization method based on deep learning
CN112115913B (en) * 2020-09-28 2023-08-25 杭州海康威视数字技术股份有限公司 Image processing method, device and equipment and storage medium
CN112364687A (en) * 2020-09-29 2021-02-12 上善智城(苏州)信息科技有限公司 Improved Faster R-CNN gas station electrostatic sign identification method and system
CN112580424B (en) * 2020-09-29 2023-08-11 长安大学 Polarization characteristic multi-scale pooling classification algorithm for complex vehicle-road environment
CN112541389B (en) * 2020-09-29 2023-08-22 西安交通大学 Transmission line fault detection method based on EfficientDet network
CN112330591B (en) * 2020-09-30 2023-01-24 中国国家铁路集团有限公司 Steel rail surface defect detection method and device capable of achieving sample-less learning
CN112215128B (en) * 2020-10-09 2024-04-05 武汉理工大学 FCOS-fused R-CNN urban road environment recognition method and device
CN112233090B (en) * 2020-10-15 2023-05-30 浙江工商大学 Film flaw detection method based on improved attention mechanism
CN112232214A (en) * 2020-10-16 2021-01-15 天津大学 Real-time target detection method based on depth feature fusion and attention mechanism
CN112329558B (en) * 2020-10-22 2022-10-25 上海交通大学 FeO content prediction method based on sintering machine tail section video
CN112329588B (en) * 2020-10-30 2024-01-05 中海石油(中国)有限公司 Pipeline fault detection method based on Faster R-CNN
CN112287854A (en) * 2020-11-02 2021-01-29 湖北大学 Building indoor personnel detection method and system based on deep neural network
CN112200803B (en) * 2020-11-04 2023-10-10 成都朴华科技有限公司 Sperm nucleoprotein maturity detection method and device
CN112380962A (en) * 2020-11-11 2021-02-19 成都摘果子科技有限公司 Animal image identification method and system based on deep learning
CN112329871B (en) * 2020-11-11 2022-03-08 河北工业大学 Pulmonary nodule detection method based on self-correction convolution and channel attention mechanism
CN112381005A (en) * 2020-11-17 2021-02-19 温州大学 Safety helmet detection system for complex scene
CN112395987B (en) * 2020-11-18 2023-07-28 西安电子科技大学 SAR image target detection method based on unsupervised domain adaptive CNN
CN112580778A (en) * 2020-11-25 2021-03-30 江苏集萃未来城市应用技术研究所有限公司 Job worker mobile phone use detection method based on YOLOv5 and Pose-animation
CN112434740A (en) * 2020-11-26 2021-03-02 西北大学 Depth learning-based Qin tomb warriors fragment classification method
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CN112396017B (en) * 2020-11-27 2023-04-07 上海建科工程咨询有限公司 Engineering potential safety hazard identification method and system based on image identification
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CN112835008B (en) * 2021-01-12 2022-03-04 西安电子科技大学 High-resolution range profile target identification method based on attitude self-adaptive convolutional network
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CN112556682B (en) * 2021-02-07 2023-06-23 天津蓝鳍海洋工程有限公司 Automatic detection algorithm for underwater composite sensor target
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CN113033609B (en) * 2021-02-16 2022-11-29 浙江大学 SAR image classification method based on multitask DCGAN
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170206431A1 (en) * 2016-01-20 2017-07-20 Microsoft Technology Licensing, Llc Object detection and classification in images
CN108416287A (en) * 2018-03-04 2018-08-17 南京理工大学 A kind of pedestrian detection method excavated based on omission negative sample
CN108830188A (en) * 2018-05-30 2018-11-16 西安理工大学 Vehicle checking method based on deep learning
CN109377555A (en) * 2018-11-14 2019-02-22 江苏科技大学 Autonomous underwater robot prospect visual field three-dimensional reconstruction target's feature-extraction recognition methods
CN109447018A (en) * 2018-11-08 2019-03-08 天津理工大学 A kind of road environment visual perception method based on improvement Faster R-CNN

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9881234B2 (en) * 2015-11-25 2018-01-30 Baidu Usa Llc. Systems and methods for end-to-end object detection
CN107507172A (en) * 2017-08-08 2017-12-22 国网上海市电力公司 Merge the extra high voltage line insulator chain deep learning recognition methods of infrared visible ray
CN107680106A (en) * 2017-10-13 2018-02-09 南京航空航天大学 A kind of conspicuousness object detection method based on Faster R CNN
CN108563977A (en) * 2017-12-18 2018-09-21 华南理工大学 A kind of the pedestrian's method for early warning and system of expressway entrance and exit
CN108648233B (en) * 2018-03-24 2022-04-12 北京工业大学 Target identification and capture positioning method based on deep learning

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170206431A1 (en) * 2016-01-20 2017-07-20 Microsoft Technology Licensing, Llc Object detection and classification in images
CN108416287A (en) * 2018-03-04 2018-08-17 南京理工大学 A kind of pedestrian detection method excavated based on omission negative sample
CN108830188A (en) * 2018-05-30 2018-11-16 西安理工大学 Vehicle checking method based on deep learning
CN109447018A (en) * 2018-11-08 2019-03-08 天津理工大学 A kind of road environment visual perception method based on improvement Faster R-CNN
CN109377555A (en) * 2018-11-14 2019-02-22 江苏科技大学 Autonomous underwater robot prospect visual field three-dimensional reconstruction target's feature-extraction recognition methods

Cited By (67)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110399884B (en) * 2019-07-10 2021-08-20 浙江理工大学 Feature fusion self-adaptive anchor frame model vehicle detection method
CN110399884A (en) * 2019-07-10 2019-11-01 浙江理工大学 A kind of adaptive anchor frame model vehicle checking method of Fusion Features
CN110414389A (en) * 2019-07-12 2019-11-05 黑龙江御林湾科技有限公司 A kind of object detection method of the fast area search based on deep learning
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CN111145239A (en) * 2019-12-30 2020-05-12 南京航空航天大学 Aircraft fuel tank redundancy automatic detection method based on deep learning
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