|Table of Contents|

Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2021年02期
Page:
222-231
Research Field:
交通信息工程及控制
Publishing date:

Info

Title:
Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm
Author(s):
MA Yong-jie1 MA Yun-ting1 CHENG Shi-sheng1 MA Yi-de2
(1. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, Gansu, China; 2. School of Information Science and Engineering, Lanzhou University, Lanzhou 730030, Gansu, China)
Keywords:
traffic image recognition convolutional neural network vehicle detection YOLO v3 model deep-SORT algorithm K-means clustering algorithm
PACS:
U491.2
DOI:
10.19818/j.cnki.1671-1637.2021.02.019
Abstract:
A vehicle detection method based on the improved YOLO v3 model and deep-SORT algorithm was proposed to address the problems of serious occlusion and high misdetection rate of small target vehicles in the real-time detection of road vehicles. To improve the detection ability of the model for road vehicle, the K-means

References:

[1] KACHACH R, CAÑAS J M. Hybrid three-dimensional and support vector machine approach for automatic vehicle tracking and classification using a single camera[J]. Journal of Electronic Imaging, 2016, 25(3): 033021.
[2] DANELLJAN M, HAGER G, KHAN F, et al. Discriminative scale space tracking[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(8): 1561-1575.
[3] WEI Yun, TIAN Qing, GUO Jian-hua, et al. Multi-vehicle detection algorithm through combining Harr and HOG features[J]. Mathematics and Computers in Simulation, 2019, 155: 130-145.
[4] 杨 娟,曹浩宇,汪荣贵,等.基于语义DCNN特征融合的细粒度车型识别模型[J].计算机辅助设计与图形学学报,2019,31(1):141-157.
YANG Juan, CAO Hao-yu, WANG Rong-gui, et al. Fine-grained car recognition model based on semantic DCNN features fusion[J]. Journal of Computer-Aided Design and Computer Graphics, 2019, 31(1): 141-157.(in Chinese)
[5] 余 烨,傅云翔,杨昌东,等.基于FR-ResNet的车辆型号精细识别研究[J/OL].自动化学报,(2019-04-03)[2020-12-22].DOI:10.16383/j.aas.c180539.
YU Ye, FU Yun-xiang, YANG Chang-dong, et al. Fine-grained car model recognition based on FR-ResNet[J/OL]. Acta Automatica Sinica,(2019-04-03)[2020-12-22]. DOI: 10.16383/j.aas.c180539.(in Chinese)
[6] 凌 艳,陈 莹.多尺度上下文信息增强的显著目标检测全卷积网络[J].计算机辅助设计与图形学学报,2019,31(11):2007- 2016.
LING Yan, CHEN Ying. Salient object detection with multiscale context enhanced fully convolution network[J].Journal of Computer-Aided Design and Computer Graphics, 2019, 31(11): 2007-2016.(in Chinese)
[7] 鞠默然,罗海波,王仲博,等.改进的YOLO v3算法及其在小目标检测中的应用[J].光学学报,2019,39(7):253-260.
JU Mo-ran, LUO Hai-bo, WANG Zhong-bo, et al. Improved YOLO v3 algorithm and its application in small target detection[J]. Acta Optica Sinica, 2019, 39(7): 253-260.(in Chinese)
[8] LI Su-hao, LIN Jin-zhao, LI Guo-quan, et al. Vehicle type detection based on deep learning in traffic scene[J]. Procedia Computer Science, 2018, 131: 564-572.
[9] 曹 磊,王 强,史润佳,等.基于改进RPN的Faster-RCNN网络SAR图像车辆目标检测方法[J].东南大学学报(自然科学版),2021,51(1):87-91.
CAO Lei, WANG Qiang, SHI Run-jia, et al. Method for vehicle target detection on SAR image based on improved RPN in Faster-RCNN[J]. Journal of Southeast University(Natural Science Edition), 2021, 51(1): 87-91.(in Chinese)
[10] 李琳辉,伦智梅,连 静,等.基于卷积神经网络的道路车辆检测方法[J].吉林大学学报(工学版),2017,47(2):384-391.
LI Lin-hui, LUN Zhi-mei, LIAN Jing, et al. Convolution neural network-based vehicle detection method[J]. Journal of Jilin University(Engineering and Technology Edition), 2017, 47(2): 384-391.(in Chinese)
[11] LEE W J, DONG S P, DONG W K, et al. A vehicle
detection using selective multi-stage features in convolutional neural networks[C]∥IEEE. 17th International Conference on Control, Automation and Systems. New York: IEEE, 2017: 1-3.
[12] ALIREZA A, LUIS G, CRISTIANO P, et al. Multimodal vehicle detection: fusing 3D-LIDAR and color camera data[J]. Pattern Recognition Letters, 2018, 115: 20-29.
[13] DAI Xue-rui. HybridNet: a fast vehicle detection system for autonomous driving[J]. Signal Processing: Image Communication, 2019, 70: 79-88.
[14] LUO Ji-qing, FANG Hu-sheng, SHAO Fa-ming, et al. Multi-scale traffic vehicle detection based on faster R-CNN with NAS optimization and feature enrichment[J]. Defence Technology, 2021, DOI: 10.1016/j.dt.2020.10.006.
[15] 邹 伟,殷国栋,刘昊吉,等.基于多模态特征融合的自主驾驶车辆低辨识目标检测方法[J/OL].中国机械工程,(2020-06-24)[2020-12-22]. https:∥kns.cnki.net/kcms/detail/42.1294.TH.20200624.1308.008.html.
ZOU Wei, YIN Guo-dong, LIU Hao-ji, et al. Low-observable targets detection method for autonomous vehicles based on multi-modal feature fusion[J/OL]. China Mechanical Engineering,(2020-06-24)[2020-12-22]. https:∥kns.cnki.net/kcms/detail/42.1294.TH.20200624.1308.008.html.(in Chinese)
[16] 汪昱东,郭继昌,王天保.一种改进的雾天图像行人和车辆检测算法[J].西安电子科技大学学报,2020,47(4):70-77.
WANG Yu-dong, GUO Ji-chang, WANG Tian-bao. Algorithm for foggy-image pedestrian and vehicle detection[J]. Journal of Xidian University, 2020, 47(4): 70-77.(in Chinese)
[17] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]∥IEEE. 27th IEEE Conference on Computer Vision and Pattern Recognition, New York: IEEE, 2014: 580-587.
[18] GIRSHICK R. Fast R-CNN[C]∥IEEE. 15th IEEE
International Conference on Computer Vision, New York: IEEE, 2015: 1440-1448.
[19] REN Shao-qing, HE Kai-ming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149.
[20] REDMON J, DIVVALA S, GIRSHICK R, et al. You only
look once: unified, real-time object detection[C]∥IEEE. 29th IEEE Conference on Computer Vision and Pattern Recognition, New York: IEEE, 2016: 779-788.
[21] REDMON J, FARHADI A. YOLO9000: better, faster,
stronger[C]∥IEEE. 30th IEEE Conference on Computer Vision and Pattern Recognition. New York: IEEE, 2017: 6517-6525.
[22] REDMON J, FARHADI A. YOLOv3: an incremental
improvement[R]. Ithaca: Cornell University, 2018.
[23] LIU WEI, ANGUELOV D, ERHAN D, et al. SSD: single shotmultibox detector[C]∥Springer. 14th European Conference on Computer Vision. Berlin: Springer, 2016: 21-37.
[24] 周 苏,支雪磊,林飞滨,等.基于车载视频图像的车辆检测与跟踪算法[J].同济大学学报(自然科学版),2019,47(S1): 191-198.
ZHOU Su, ZHI Xue-lei, LIN Fei-bin, et al. Research on vehicle detection and tracking algorithm based on onboard video images[J]. Journal of Tongji University(Natural Science Edition), 2019, 47(S1): 191-198.(in Chinese)
[25] 金立生,郭柏苍,王芳荣,等.基于改进YOLOv3的车辆前方动态多目标检测算法[J/OL].吉林大学学报(工学版),(2020-12-17)[2020-12-22]. https:∥doi.org/10.13229/j.cnki.jdxbgxb20200588.
JIN Li-sheng, GUO Bai-cang, WANG Fang-rong. Dynamic multiple object algorithm for vehicle forward based on improved YOLOv3[J/OL]. Journal of Jilin University(Engineering and Technology Edition),(2020-12-17)[2020-12-22]. https:∥doi.org/10.13229/j.cnki.jdxbgxb20200588.(in Chinese)
[26] 李 珣,刘 瑶,李鹏飞,等.基于Darknet框架下YOLO v2算法的车辆多目标检测方法[J].交通运输工程学报,2018,18(6):142-158.
LI Xun, LIU Yao, LI Peng-fei, et al. Vehicle multi-target detection method based on YOLO v2 algorithm under darknet framework[J]. Journal of Traffic and Transportation Engineering, 2018, 18(6): 142-158.(in Chinese)
[27] 黎 洲,黄妙华.基于YOLO_v2模型的车辆实时检测[J].中国机械工程,2018,29(15):1869-1874.
LI Zhou, HUANG Miao-hua. Vehicle detections based on YOLO_v2 in real-time[J]. China Mechanical Engineering, 2018, 29(15): 1869-1874.(in Chinese)
[28] 刘 军,后士浩,张 凯,等.基于增强Tiny YOLOV3算法的车辆实时检测与跟踪[J].农业工程学报,2019,35(8):118-125.
LIU Jun, HOU Shi-hao, ZHANG Kai, et al. Real-time vehicle detection and tracking based on enhanced Tiny YOLOV3 algorithm[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(8): 118-125.(in Chinese)
[29] SRI J S, ESTHER R P. Little YOLO-SPP: a delicate real-time vehicle detection algorithm[J]. Optik, 2021, 225: 165818.
[30] 柳长源,王 琪,毕晓君.多目标小尺度车辆目标检测方法的研究[J/OL].控制与决策,(2020-09-03)[2020-12-22]. https:∥doi.org/10.13195/j.kzyjc.2020.0635.
LIU Chang-yuan, WANG Qi, BI Xiao-juan. Research on multi-target and small-scale vehicle target detection method[J/OL]. Control and Decision,(2020-09-03)[2020-12-22]. https:∥doi.org/10.13195/j.kzyjc.2020.0635.(in Chinese)
[31] LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft
COCO: common objects in context[C]∥Springer. 13th European Conference on Computer Vision, 2014. Berlin: Springer, 2014: 740-755.
[32] 王宇宁,庞智恒,袁德明.基于YOLO算法的车辆实时检测[J].武汉理工大学学报,2016,38(10):42-46.
WANG Yu-ning, PANG Zhi-heng, YUAN De-ming. Vehicle detection based on YOLO in real time[J]. Journal of Wuhan University of Technology, 2016, 38(10): 42-46.(in Chinese)

Memo

Memo:
-
Last Update: 2021-06-01