Volume 21 Issue 2
Aug.  2021
Turn off MathJax
Article Contents
Article Navigation >  Journal of Traffic and Transportation Engineering  > 2021  >  21(2): 222-231
Next Previous
MA Yong-jie, MA Yun-ting, CHENG Shi-sheng, MA Yi-de. Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 222-231. doi: 10.19818/j.cnki.1671-1637.2021.02.019
Citation: MA Yong-jie, MA Yun-ting, CHENG Shi-sheng, MA Yi-de. Road vehicle detection method based on improved YOLO v3 model and deep-SORT algorithm[J]. Journal of Traffic and Transportation Engineering, 2021, 21(2): 222-231. doi: 10.19818/j.cnki.1671-1637.2021.02.019
shu

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

doi: 10.19818/j.cnki.1671-1637.2021.02.019
  • 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

Funds:

National Natural Science Foundation of China 62066041

More Information
  • Author Bio:

    MA Yong-jie(1967-), male, professor, PhD, myjmyj@nwnu.edu.cn

  • Received Date: 2020-11-23
  • Publish Date: 2021-04-25
    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++ clustering algorithm was used to cluster the target candidate boxes, the appropriate number of anchor boxes was selected, and a feature extraction layer to the shallow layer of the network was added to extract more refined vehicle features. The robustness of the network for different distant targets was enhanced by retaining the original YOLO v3 model's output layer but adding another layer to it. After the 52 pixel×52 pixel output feature map was upsampled, a 104 pixel×104 pixel feature map was obtained, which was spliced with a shallow layer feature map of the same size to achieve the vehicle target detection. To reduce the influence of target occlusion on the detection and improve the attention to the association information between the upper and lower frames of the video, the YOLO v3 model was improved and combined with the deep-SORT algorithm to compensate for their shortcomings. Experimental results show that the improved YOLO v3 model can enhance the vehicle detection performance. Compared with the model adding feature extraction layer in the shallow layer of the network, the average accuracy improves by 1.4%, and compared with the model adding one output layer, the average accuracy improves by 0.8%. It indicates that the improved YOLO v3 model has a stronger feature expression ability and enhances the network's ability to detect small targets. After the deep-SORT algorithm is introduced into the improved YOLO v3 model, the precision and recall are 90.16% and 91.34%, respectively. Compared with the improved YOLO v3 model, the precision and recall increase by 1.48% and 4.20%, respectively. At the same time, the detection speed is maintained, and the detection of different-sized targets is highly robust. 4 tabs, 5 figs, 32 refs.

     

    • FullText(HTML)
  • loading
    • References(32)
  • [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. doi: 10.1117/1.JEI.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. doi: 10.1109/TPAMI.2016.2609928
    [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. doi: 10.1016/j.matcom.2017.12.011
    [4]
    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) http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201901018.htm
    [5]
    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]
    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) http://www.cnki.com.cn/Article/CJFDTOTAL-JSJF201911015.htm
    [7]
    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) http://www.cnki.com.cn/Article/CJFDTOTAL-GXXB201907028.htm
    [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. doi: 10.1016/j.procs.2018.04.281
    [9]
    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]
    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) http://www.cnki.com.cn/Article/CJFDTOTAL-JLGY201702006.htm
    [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. doi: 10.1016/j.patrec.2017.09.038
    [13]
    DAI Xue-rui. HybridNet: a fast vehicle detection system for autonomous driving[J]. Signal Processing: Image Communication, 2019, 70: 79-88. doi: 10.1016/j.image.2018.09.002
    [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]
    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]. http://kns.cnki.net/kcms/detail/42.1294.TH.20200624.1308.008.html. (in Chinese)
    [16]
    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) http://www.cnki.com.cn/Article/CJFDTOTAL-XDKD202004013.htm
    [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. doi: 10.1109/TPAMI.2016.2577031
    [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]
    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) http://www.cnki.com.cn/Article/CJFDTOTAL-TJDZ2019S1036.htm
    [25]
    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]. http://doi.org/10.13229/j.cnki.jdxbgxb20200588. (in Chinese)
    [26]
    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) doi: 10.3969/j.issn.1671-1637.2018.06.015
    [27]
    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) doi: 10.3969/j.issn.1004-132X.2018.15.015
    [28]
    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) http://www.cnki.com.cn/Article/CJFDTOTAL-NYGU201908014.htm
    [29]
    SRI J S, ESTHER R P. Little YOLO-SPP: a delicate real-time vehicle detection algorithm[J]. Optik, 2021, 225: 165818. doi: 10.1016/j.ijleo.2020.165818
    [30]
    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]. http://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]
    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) http://www.cnki.com.cn/Article/CJFDTOTAL-WHGY201610009.htm
    • Relative Articles
    • Supplements(0)
    • Cited By

Catalog

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (2804) PDF downloads(322) Cited by()
    Related

    Website copyright Journal of Traffic and Transportation Engineering Editorial Department陕ICP备05001904号-1

    Address ：Editorial Department of Journal of Traffic and Transportation Engineering, Chang'an University, Middle Section of South 2nd Ring Road, Xi'an, China（710064） Tel：029-82334388 Email：jygc@gfscw.cn

    All visit:Today's visit:

    Supported by: Beijing Renhe Information Technology Co. Ltd    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return