Improved Traffic Sign Detection Algorithm Based on Libra R-CNN

doi:10.3901/JME.2021.22.255

Abstract

Abstract: In order to study the potential benefits and constraints of controlling vehicle braking in reducing ground related injury, 139 real world pedestrian-vehicle accidents are selected from the pre-accumulated accident database firstly, and then all selected accidents are reconstructed by PC-Crash, after that a vehicle braking method is applied to each reconstructed cases, finally the injury in various parts of human body, collision location of the head-car and head- ground, and temporal/spatial constraints during braking control are collected. Results show that the vehicle full braking after the impact does not have a significant influence on pedestrian injury in real world accidents; through controlling vehicle braking, the ground related head/pelvis injury can be reduced significantly but the vehicle related injury will not increase, and the coincidence rate of the collision position of head-car and head-ground can be reduced; but controlling vehicle braking requires the vehicle to make a judgment in a short time to correctly control the vehicle and 8.4% of cases do not have enough space for the vehicle to carry out a braking control.

Key words: computer vision, deep learning, target detection, traffic sign detection, improved Libra R-CNN, GA-RPN

CLC Number:

U495

ZHAO Zijing, LIU Hongzhe, CAO Dongpu. Improved Traffic Sign Detection Algorithm Based on Libra R-CNN[J]. Journal of Mechanical Engineering, 2021, 57(22): 255-265.

References

[1] HAN Cen, GAO Guangyu, ZHANG Yu. Real-time small traffic sign detection with revised faster-RCNN[J]. Multimedia Tools and Applications, 2019, 78(10):13263-13278.
[2] SONG Shijin, QUE Zhiqiang, HOU Junjie, et al. An efficient convolutional neural network for small traffic sign detection[J]. Journal of Systems Architecture, 2019, 97:269-277.
[3] WALI SAFAT B, ABDULLAH MAJID A, HANNANMAHAMMAD A, et al. Vision-based traffic sign detection and recognition systems:Current trends and challenges[J]. Sensors(Basel, Switzerland), 2019, 19(9):2093.
[4] 董朋林. 街道实景中的交通标志检测与识别[J]. 现代计算机, 2019(36):37-43. DONG Penglin. Detection and recognition of traffic signs in the real scene of street[J]. Modern Computer, 2019(36):37-43.
[5] PANG J, CHEN K, SHI J, et al. Libra R-CNN:Towards balanced learning for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019:821-830.
[6] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems, 2012:1097-1105.
[7] VISHWAKARMA S, AGRAWAL A. A survey on activity recognition and behavior understanding in video surveillance[J]. Visual Computer, 2013, 29(10):983-1009.
[8] 周晓彦, 王珂, 李凌燕. 基于深度学习的目标检测算法综述[J]. 电子测量技术, 2017, 40(11):89-93. ZHOU Xiaoyan, WANG Ke, LI Lingyan. Review of object detection based on deep learning[J]. Electronic Measurement Technology, 2017, 40(11):89-93.
[9] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:Unified, real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition, 2016:779-788.
[10] LIU W, ANGUELOV D, ERHAN D, et al. SSD:Single shot multibox detector[C]//European Conference on Computer Vision. Springer, Cham, 2016:21-37.
[11] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEEInternational Conference on Computer Vision, 2015:1440-1448.
[12] REN S, HE K, GIRSHICK R, et al. Faster R-CNN:Towards real-time object detection with region proposal networks[C]//Advances in Neural Information Processing Systems, 2015:91-99.
[13] HE K, GKIOXARI G, DOLLÁR P, et al. Mask R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision, 2017:2961-2969.
[14] CAI Z, VASCONCELOS N. Cascade R-CNN:Delving into high quality object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:6154-6162.
[15] REDMON J, FARHADI A. YOLO9000:Better, faster, stronger[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017:7263-7271.
[16] REDMON J, FARHADI A. YOLOv3:An incremental improvement[R]. ar Xiv preprint ar Xiv:1804. 02767, 2018.
[17] YANG B, YAN J, LEI Z, et al. Craft objects from images[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016:6043-6051.
[18] HUANG S W, LIN C T, CHEN S P, et al. Auggan:Cross domain adaptation with GAN-based data augmentation[C]//Proceedings of the European Conference on Computer Vision (ECCV), 2018:718-731.
[19] WANG X, SHRIVASTAVA A, GUPTA A. A-fast-RCNN:Hard positive generation via adversary for object detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2017:2606-2615.
[20] 单义, 杨金福, 武随烁, 等. 基于跳跃连接金字塔模型的小目标检测[J]. 智能系统学报, 2019, 14(6):1144-1151. SHAN Yi, YANG Jinfu, WU Suishuo, et al. Skip feature pyramid network with a global receptive field for small object detection[J]. CAAI Transactions on Intelligent Systems, 2019, 14(6):1144-1151.
[21] WANG J, CHEN K, YANG S, et al. Region proposal by guided anchoring[C]//Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, 2019:2965-2974.
[22] REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union:A metric and a loss for bounding box regression[C]//Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, 2019:658-666.
[23] WANG X, GIRSHICK R, GUPTA A, et al. Non-local neural networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018:7794-7803.
[24] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEEConference on Computer Vision and Pattern Recognition, 2016:770-778.