Computer Science

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

Special Issue: Intelligent Edge Computing

• 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).

PDF (PC)

884

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

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].http://pjreddie.com/darknet/.
[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] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[2] CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[3] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[4] LIU Dong-mei, XU Yang, WU Ze-bin, LIU Qian, SONG Bin, WEI Zhi-hui. Incremental Object Detection Method Based on Border Distance Measurement [J]. Computer Science, 2022, 49(8): 136-142.
[5] WANG Can, LIU Yong-jian, XIE Qing, MA Yan-chun. Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization [J]. Computer Science, 2022, 49(8): 157-164.
[6] DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119.
[7] LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(7): 127-131.
[8] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[9] WU Zi-bin, YAN Qiao. Projected Gradient Descent Algorithm with Momentum [J]. Computer Science, 2022, 49(6A): 178-183.
[10] HU Cong, HE Xiao-hui, SHAO Fa-ming, ZHANG Yan-wu, LU Guan-lin, WANG Jin-kang. Traffic Sign Detection Based on MSERs and SVM [J]. Computer Science, 2022, 49(6A): 325-330.
[11] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[12] YANG Jian-nan, ZHANG Fan. Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure [J]. Computer Science, 2022, 49(6A): 353-357.
[13] ZHANG Jia-hao, LIU Feng, QI Jia-yin. Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer [J]. Computer Science, 2022, 49(6A): 370-377.
[14] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[15] CHEN Yong-ping, ZHU Jian-qing, XIE Yi, WU Han-xiao, ZENG Huan-qiang. Real-time Helmet Detection Algorithm Based on Circumcircle Radius Difference Loss [J]. Computer Science, 2022, 49(6A): 424-428.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.