基于YOLOP-L的多特征融合道路全景驾驶检测

doi:10.11896/jsjkx.230700185

Abstract

Abstract: In recent years,traffic image detection technology from the driver’s perspective has become an important research direction in the field of transportation,and extracting various features such as vehicles,roads,and traffic signs has become an urgent task for drivers to understand the diversity of road information.Previous studies have made significant progress in feature extraction for single class object detection.However,these studies cannot be well applied to other feature detection with significant differences,and the accuracy of individual feature detection will be lost during fusion training.In response to the diverse and complex road information within the driver’s field of view,this paper proposes a detection model YOLOP-L based on multi feature fusion training.It can simultaneously fuse and train multiple different feature traffic targets,while ensuring the accuracy of individual detection tasks.The results indicate that YOLOP-L can effectively solve the problems of insufficient detection accuracy and missing segmentation in complex scenes on the challenging BDD100K dataset,improving the accuracy and robustness of vehicle recognition,lane line detection,and joint training of road driving areas.Finally,comparative experiments show that YOLOP-L runs faster than the original YOLOP network.The recall rate increases by 2.2% under the vehicle target detection task.In the lane detection task,the accuracy improves by 2.8%,and the IoU value of the lane line decreases by 2.45% compared to the HybridNets network,but increases by 1.95% compared to the YOLOP-L network.Its overall detection performance improves by 1.1% under the task of driving area segmentation.The results indicate that YOLOP-L can effectively solve the problems of insufficient detection accuracy and missing segmentation in complex scenes on the challenging BDD100K dataset,improving the accuracy and robustness of vehicle recognition,lane line detection,and joint training of road driving areas.

Key words: Panoramic driving, Multi featurefusion, Vehicle inspection, Travelable area detection, Lane line detection, Bidirectional feature pyramid network

CLC Number:

TP391

LYU Jialu, ZHOU Li, JU Yongfeng. Multi Feature Fusion for Road Panoramic Driving Detection Based on YOLOP-L[J].Computer Science, 2024, 51(6A): 230700185-8.

References

[1]ZOU Q,JIANG H,DAI Q,et al.Robust Lane Detection From Continuous Driving Scenes Using Deep Neural Networks[J].IEEE Transactions on Vehicular Technology,2020,69(1):41-54.
[2]SEO Y W,RAJKUMAR R R.Detection and tracking of boundary of unmarked roads[C]//International Conference on Information Fusion.IEEE,2014.
[3]DONG-SI T C,GUO D,YAN C H,et al.Robust extraction of shady roads for vision-based UGV navigation[C]//2008 IEEE/RSJ International Conference on Intelligent Robots and Systems,Acropolis Convention Center,Nice,France.IEEE,2008.
[4]KALAKI A S,SAFABAKHSH R.Current and adjacent lanesdetection for an autonomous vehicle to facilitate obstacle avoidance using a monocular camera[C]//2014 Iranian Conference on Intelligent Systems(ICIS).IEEE,2014:1-6.
[5]YU L,TAN H,BANSAL M,et al.A Joint Speaker-Listener-Reinforcer Model for Referring Expressions[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2016:3521-3529.
[6]WU F,XU Z,YANG Y,et al.An End-to-End Approach to Natural Language Object Retrieval via Context-Aware Deep Reinforcement Learning[J].arXiv:1703.07579,2017.
[7]DUAN K,XIE L,QI H,et al.Corner Proposal Network for Anchor-free,Two-stage Object Detection[C]//European Conference on Computer Vision.2020.
[8]REN S,HE K,GIRSHICK R,et al.Faster R-CNN:TowardsReal-Time Object Detection with Region Proposal Networks[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence.2017:1137-1149.
[9]ROSS G.Fast R-CNN[C]//2015 IEEE International Confe-rence on Computer Vision(ICCV).2015:1440-1448.
[10]HE K,ZHANG X,REN S,et al.Spatial Pyramid Pooling inDeep Convolutional Networks for Visual Recognition[C]//IEEE Transactions on Pattern Analysis and Machine Intelligence.2015:1904-1916.
[11]LIU W,ANGUELOV D,ERHAN D.et al.SSD:Single ShotMultiBox Detector[C]//European Conference on Computer Vision.2015.
[12]MA J,SHAO W,YE H,et al.Arbitrary-Oriented Scene Text Detection via Rotation Proposals[C]//IEEE Transactions on Multimedia.2018:3111-3122.
[13]REDMON J,DIVVALA S,GIRSHICKET R,et al.You Only Look Once:Unified,Real-Time Object Detection[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2015:779-788.
[14]TAN M X,PANG R,LE Q V,et al.EfficientDet:Scalable and Efficient Object Detection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2019:10778-10787.
[15]PAN X G,et al.Spatial As Deep:Spatial CNN for Traffic Scene Understanding[C]//AAAI Conference on Artificial Intelligence.2017.
[16]ZHENG T,FANG H,ZHANG Y,et al.RESA:Recurrent Feature-Shift Aggregator for Lane Detection[C]//AAAI Conference on Artificial Intelligence.2020.
[17]LI X,LI J,HU X,et al.Line-CNN:End-to-End Traffic Line Detection With Line Proposal Unit[C]//IEEE Transactions on Intelligent Transportation Systems.2020:248-258.
[18]TABELINI L,BERRIEL R,PAIXAO T M,et al.Keep your Eyes on the Lane:Real-time Attention-guided Lane Detection[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2020:294-302.
[19]TABELINI L,BERRIEL R,PAIXAO T M,et al.PolyLaneNet:Lane Estimation via Deep Polynomial Regression[C]//2020 25th International Conference on Pattern Recognition(ICPR).2020:6150-6156.
[20]FENG Z Y,GUO S,TAN X,et al.Rethinking Efficient LaneDetection via Curve Modeling[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR).2022:17041-17049.
[21]DONG-SI T C,GUO D,YAN C H,et al Robust extraction of shady roads for vision-based UGV navigation[C]//2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.Acropolis Convention Center,Nice,France.IEEE,2008.
[22]MA B,LAKSHMANAN S,HERO A,et al.Simultaneous detection of lane and pavementboundaries using model-based multisensor fusion[J].IEEE Trans.Intell.Transp.Syst.,2000:135-147.
[23]ZHOU L,FANG J,JU Y,et al.Multi-Saliency Detection via Instance Specific Element Homology[C]//2017 International Conference on Digital Image Computing:Techniques and Applications(DICTA).Sydney,NSW,Australia,2017:1-8.
[24]XU Z H,LIU Y,GAN L,et al.RNGDet:Road Network Graph Detection by Transformer in Aerial Images[C]//IEEE Transactions on Geoscience and Remote Sensing.2022.
[25]WU D S,LIAO M,ZHANG W,et al.YOLOP:You Only Look Once for Panoptic Driving Perception[C]//Machine Intelligence Research.2021:550-562.
[26]VU D,NGO B,PHAN H N,et al.HybridNets:End-to-End Perception Network[J].arXiv:2203.09035,2022.

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Multi Feature Fusion for Road Panoramic Driving Detection Based on YOLOP-L

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