Computer Science ›› 2021, Vol. 48 ›› Issue (1): 89-95.doi: 10.11896/jsjkx.200800034

• Intelligent Edge Computing • Previous Articles     Next Articles

L-YOLO:Real Time Traffic Sign Detection Model for Vehicle Edge Computing

SHAN Mei-jing, QIN Long-fei, ZHANG Hui-bing   

  1. Department of Information Science and Technology,East China University of Political Science and Law,Shanghai 201620,China
    Guangxi Key Lab of Trusted Software,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Received:2020-08-05 Revised:2020-12-12 Online:2021-01-15 Published:2021-01-15
  • About author:SHAN Mei-jing,born in 1979,Ph.D,associate professor.Her main research interests include cybercrime and compu-ter forensics and machine learning.
    ZHANG Hui-bing,born in 1976,Ph.D,associate professor,is a member of China Computer Federation.His main research interests include Internet of Things,edge computing and social computing.
  • Supported by:
    National Social Science Fund of China(16BFX085).

Abstract: In the vehicle edge computing unit,due to the limited resources of its hardware equipment,it becomes more and more urgent to develop a lightweight and efficient traffic sign detection model for vehicle edge computing.This paper proposes a lightweight traffic sign detection model based on Tiny YOLO,which is called L-YOLO.Firstly,L-YOLO uses partial residual connection to enhance the learning ability of lightweight network.Secondly,in order to reduce the false detection and missed detection of traffic signs,L-YOLO uses Gauss loss function as the location loss of boundary box.In the traffic sign detection dataset named TAD16K,the parameter amount of L-YOLO is 18.8M,the calculation amount is 8.211BFlops,the detection speed is 83.3FPS,and the mAP reaches 86%.Experimental results show that the algorithm not only guarantees the real-time performance,but also improves the detection accuracy.

Key words: Vehicle edge computing, Object detection, Traffic sign detection, Convolutional neural network, Residual connection, Tiny YOLO

CLC Number: 

  • TP391
[1] ZHU Z,LIANG D,ZHANG S,et al.Traffic-sign detection and classification in the wild[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2016:2110-2118.
[2] LI Y,WANG J,XING T,et al.TAD16K:An enhanced benchmark for autonomous driving[C]//IEEE International Confe-rence on Image Processing.IEEE Computer Society,2017:2344-2348.
[3] YANG T,LONG X,SANGAIAH A K,et al.Deep detectionnetwork for real-life traffic sign in vehicular networks[J].Computer Networks,2018,136(8):95-104.
[4] LU Y,JIA M,ZHANG S,et al.Traffic signal detection and classification in street views using an attention model[J].Computational Visual Media,2018,4(3):253-266.
[5] REDMON J,DIVVALA S,GIRSHICK R,et al.You only look once:unified,real-time object detection[C]//IEEE Conference on Computer Vision And Pattern Recognition.IEEE Computer Society,2016:779-788.
[6] REDMON J,FARHADI A.YOLO9000:Better,Faster,Stronger[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2017:7263-7271.
[7] REDMON J,FARHADI A.YOLOv3:An Incremental Improvement[J].arXiv:1804.02767,2018.
[8] WANG C Y,LIAO H Y M,CHEN P Y,et al.Enriching variety of layer-wise learning information by gradient combination[C]//IEEE/CVF International Conference on Computer Vision Workshop.IEEE Computer Society,2019:2477-2484.
[9] CHOI J,CHUN D,KIM H,et al.Gaussian YOLOv3:An accurate and fast object detector using localization uncertainty for autonomous driving[J].arXiv:1904.04620v2,2019.
[10] GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2014:580-587.
[11] UIJLINGS J R R,VAN DE SANDE K E A,GEVERS T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171.
[12] HE K M,ZHANG X Y,REN S Q,et al.Spatial pyramid pooling in deep convolutional networks for visual Recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2015,37(9):1904-1916.
[13] GIRSHICK,ROSS.Fast R-CNN[C]//IEEE International Conference on Computer vision.IEEE Computer Society,2015:1440-1448.
[14] REN S,HE K,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Trans.Pattern Anal.Mach.Intell.,2017,39:1137-1149.
[15] LIU W,ANGUELOV D.SSD:Single Shot MultiBox Detector[C]//The 14th European Conference on Computer Vision.2016:21-37.
[16] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//3rd International Conference on Learning Representations.2015:1556.
[17] HE K,ZHANG X,REN S,et al.Deep Residual Learning for Image Recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2016:770-778.
[18] LIN T Y,DOLLAR P,GIRSHICK R,et al.Feature pyramidnetworks for object detection[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2017:2117-2125.
[19] HE K,GKIOXARI G,DOLLAR P,et al.Mask R-CNN[C]//IEEE international conference on computer vision.IEEE Computer Society,2017:2961-2969.
[20] NEUBECK A,GOOL,Van L J.Efficient Non-Maximum Suppression[C]//International Conference on Pattern Recognition.IEEE Computer Society,2006:850-855.
[21] STALLKAMP J,SCHLIPSING M,SALMEN J,et al.The germantraffic sign recognition benchmark:a multi-class classificationcompetition[C]//The International Joint Conference on Neural Networks.IEEE Computer Society,2011:1453-1460.
[22] GEIGER A,LENZ P,URTASUN R.Are we ready for autonomous driving? The kitti vision benchmark suite [C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE Computer Society,2012:3354-3361.
[23] REDMON J.Darknet:Open Source Neural Networks in C,2013-2016 [OL].
[24] LIN T Y,MAIRE M,BELONGIE S,et al.Microsoft COCO:Common objectsin context[C]//The 12th European Conference on Computer Vision.2014:740-755.
[1] HE Yan-hui, WU Gui-xing, WU Zhi-qiang. Domain Alignment Based Object Detection of X-ray Images [J]. Computer Science, 2021, 48(1): 175-181.
[2] LI Ya-nan, HU Yu-jia, GAN Wei, ZHU Min. Survey on Target Site Prediction of Human miRNA Based on Deep Learning [J]. Computer Science, 2021, 48(1): 209-216.
[3] ZHANG Jia-jia, ZHANG Xiao-hong. Multi-branch Convolutional Neural Network for Lung Nodule Classification and Its Interpretability [J]. Computer Science, 2020, 47(9): 129-134.
[4] ZHU Ling-ying, SANG Qing-bing, GU Ting-ting. No-reference Stereo Image Quality Assessment Based on Disparity Information [J]. Computer Science, 2020, 47(9): 150-156.
[5] CUI Tong-tong, WANG Gui-ling, GAO Jing. Ship Trajectory Classification Method Based on 1DCNN-LSTM [J]. Computer Science, 2020, 47(9): 175-184.
[6] QI Shao-hua, XU He-gen, WAN You-wen, FU Hao. Construction of Semantic Mapping in Dynamic Environments [J]. Computer Science, 2020, 47(9): 198-203.
[7] LIU Hai-chao, WANG Li. Graph Classification Model Based on Capsule Deep Graph Convolutional Neural Network [J]. Computer Science, 2020, 47(9): 219-225.
[8] HUANG Jin-xing, PAN Xiang, ZHENG He-rong. End-to-end Network Structure Optimization of Scene Text Recognition Based on Residual Connection [J]. Computer Science, 2020, 47(8): 221-226.
[9] LIANG Zheng-you, HE Jing-lin, SUN Yu. Three-dimensional Convolutional Neural Network Evolution Method for Facial Micro-expression Auto-recognition [J]. Computer Science, 2020, 47(8): 227-232.
[10] SUN Yan-li, YE Jiong-yao. Convolutional Neural Networks Compression Based on Pruning and Quantization [J]. Computer Science, 2020, 47(8): 261-266.
[11] LIU Xiao, YUAN Guan, ZHANG Yan-mei, YAN Qiu-yan, WANG Zhi-xiao. Hand Gesture Recognition Based on Self-adaptive Multi-classifiers Fusion [J]. Computer Science, 2020, 47(7): 103-110.
[12] CHENG Zhe, BAI Qian, ZHANG Hao, WANG Shi-pu and LIANG Yu. Improving Hi-C Data Resolution with Deep Convolutional Neural Networks [J]. Computer Science, 2020, 47(6A): 70-74.
[13] HE Lei, SHAO Zhan-peng, ZHANG Jian-hua and ZHOU Xiao-long. Review of Deep Learning-based Action Recognition Algorithms [J]. Computer Science, 2020, 47(6A): 139-147.
[14] SUN Zheng and WANG Xin-yu. Application of Deep Learning in Photoacoustic Imaging [J]. Computer Science, 2020, 47(6A): 148-152.
[15] LUO Jin-nan and ZHANG Ji-min. Rail Area Extraction Using Extended Haar-like Features and DBSCAN Clustering [J]. Computer Science, 2020, 47(6A): 153-156.
Full text



[1] GUO Jun-xia, GUO Ren-fei, XU Nan-shan and ZHAO Rui-lian. Study on Construction of EFSM Model for Web Application Based on Session[J]. Computer Science, 2018, 45(4): 203 -207 .
[2] XIANG Ying-zhuo, TAN Ju-xian, HAN Jie-si, SHI Hao. Survey of Graph Matching Algorithms[J]. Computer Science, 2018, 45(6): 27 -31 .
[3] JIN Rui, LIU Zuo-xue. Synchronization Protocol of TDMA Ad hoc Network Based on Time Slot Alignment[J]. Computer Science, 2018, 45(6): 84 -88 .
[4] JI Hai-juan, ZHOU Cong-hua, LIU Zhi-feng. Symbolic Aggregate Approximation Method of Time Series Based on Beginning and End Distance[J]. Computer Science, 2018, 45(6): 216 -221 .
[5] CHEN Fu-cai, LI Si-hao, ZHANG Jian-peng, HUANG Rui-yang. Multi-label Feature Selection Algorithm Based on Improved Label Correlation[J]. Computer Science, 2018, 45(6): 228 -234 .
[6] CHEN Rong, LI Peng, HUANG Yong. Moving Shadow Removal Algorithm Based on Multi-feature Fusion[J]. Computer Science, 2018, 45(6): 291 -295 .
[7] TIAN Xiao-yan, WEI Na, FAN Ze-ming and ZHANG Suo-liang. Study and Design of Interleaver for Repeat Accumulate Codes[J]. Computer Science, 2018, 45(5): 79 -82 .
[8] AO Quan, LU Hui-mei, XIANG Yong and CAO Rui-dong. QEMU Based Abnormal Communication Analysis of Linux Applications[J]. Computer Science, 2018, 45(5): 89 -96 .
[9] PAN Ming-ming, LI Ding-ding, TANG Yong and LIU Hai. Design and Implemention of Accessing Hybrid Database Systems Based on Middleware[J]. Computer Science, 2018, 45(5): 163 -167 .
[10] XU Feng-sheng, YU Xiu-qing and SHI Kai-quan. P-data Model and Intelligent Acquisition of P-data[J]. Computer Science, 2018, 45(5): 176 -179 .