Volume 42 Issue 1
Feb.  2024
Turn off MathJax
Article Contents
Article Navigation >  Journal of Transport Information and Safety  > 2024  >  42(1): 115-123
ZHANG Yifan, NIE Linzhen, HUANG Haoran, YIN Zhishuai. A Method of Real-time Detection for Road Traffic Participants Based on an Improved YOLOv5 Algorithm[J]. Journal of Transport Information and Safety, 2024, 42(1): 115-123. doi: 10.3963/j.jssn.1674-4861.2024.01.013
Citation: ZHANG Yifan, NIE Linzhen, HUANG Haoran, YIN Zhishuai. A Method of Real-time Detection for Road Traffic Participants Based on an Improved YOLOv5 Algorithm[J]. Journal of Transport Information and Safety, 2024, 42(1): 115-123. doi: 10.3963/j.jssn.1674-4861.2024.01.013
shu

A Method of Real-time Detection for Road Traffic Participants Based on an Improved YOLOv5 Algorithm

doi: 10.3963/j.jssn.1674-4861.2024.01.013
  • 1.

    College of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

  • 2.

    Hubei Engineering and Technology Center of New Energy and Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan 430070, China

  • 3.

    Dongfeng Commercial Vehicle Technical Center, Dongfeng Commercial Vehicle Co., Ltd., Wuhan 430056, China

  • Received Date: 2023-10-15
    Available Online: 2024-05-31
    Abstract
  • Rapidly and accurately detecting traffic participants from road surveillance images is of great significance for intelligent transportation systems to monitor road targets. With the aim of solving the issues low detection accuracy and disability of detecting overlapping targets of the original YOLOv5 algorithm for various traffic participants, a real-time detection method of road traffic participants based on an improved YOLOv5 algorithm is proposed. To improve the capacity of shallow network to extract image characteristics, the fused mobile inverted bottleneck convolution (FusedMBC) is adopted to replace the original convolution structure to speed up the reasoning speed of the shallow neural network, and the self-attention mechanism is used to learn the texture features of traffic participants To enhance the ability of backbone network to perceive spatial features of images, the coordinate attention mechanism (CA) is introduced, which makes the backbone network pay more attention to the semantic characteristics of traffic participants in the images. To enable conventional convolution to capture visual layouts and enhance the sensitivity of activation space, the funnel activation function (FReLU) is adopted as the activation function of the convolution layer, and the feature vector can be modeled at the pixel level. To enhance the ability of extracting spatial features for dense targets, a coordinate attention mechanism is introduced to the feature fusion net-work, which captures the spatial and channel feature information of densely fused targets through attention mecha-nism, the network can accurately locate each target. Through data enhancement preprocessing on images of traffic participants based on the data set DAIR-V2X about vehicle-road cooperative and autonomous driving, a test set of 2 000 images is developed to verify the property of the model. Experimental results show that: ①The improved YOLOv5 algorithm has a mean average precision of 82.4%, an average recall rate of 93%, and an average detection speed of 204 frames/s. ②In comparison to the original YOLOv5, its average detection accuracy and average detection speed are increased by 5.8% and 33.3%, respectively. These results verify that the proposed method can detect traffic participants quickly and accurately, which can help to improve the ability of supervising traffic participants for intelligent transportation systems.

     

    • FullText(HTML)
  • loading
    • References(27)
  • [1]
    中国国家统计局. 中国统计年鉴-2022[EB/OL]. (2023-02-15)[2023-06-05]. http://www.stats.gov.cn/sj/ndsj/2022/indexch.htm.

    National Bureau of Statistics of China. China statistical year-book-2022[EB/OL]. (2023-02-15)[2023-06-05]. http://www.stats.gov.cn/sj/ndsj/2022/indexch.htm. (in Chinese)
    [2]
    高磊, 李超, 朱成军, 等. 基于边缘对称性的视频车辆检测算法[J]. 北京航空航天大学学报, 2008(9): 1113-1116. https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK200809029.htm

    GAO L, LI C, ZHU C J, et al. Video vehicle detection algorithm based on edge symmetry[J]. Journal of Beijing University of Aeronautics and Astronautics, 2008(9): 1113-1116. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK200809029.htm
    [3]
    黄飞. 基于HOG特征和改进IKSVM的车辆检测算法[J]. 安徽工程大学学报, 2016, 31(5): 28-32. https://www.cnki.com.cn/Article/CJFDTOTAL-AHJD201605007.htm

    HUANG F. Vehicle detection algorithm based on HOG features and improved IKSVM[J]. Journal of Anhui Engineering University, 2016, 31(5): 28-32. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-AHJD201605007.htm
    [4]
    王琳. 基于HOG和SVM的车辆检测算法研究[D]. 武汉: 华中科技大学, 2017.

    WANG L. Research on vehicle detection algorithm based on HOG and SVM[D]. Wuhan: Huazhong University of Science and Technology, 2017. (in Chinese)
    [5]
    CHENG W C, JHAN D M. A self-constructing cascade classifier with AdaBoost and SVM for pedestriandetection[J]. Engineering Applications of Artificial Intelligence, 2013, 26(3): 1016-1028. doi: 10.1016/j.engappai.2012.08.013
    [6]
    樊春年, 杜卫平, 刘艳荣. 基于HOG特征结合Adaboost算法的行人检测[J]. 自动化技术与应用, 2018, 37(7): 89-91. https://www.cnki.com.cn/Article/CJFDTOTAL-ZDHJ201807022.htm

    FAN C N, DU W P, LIU Y R. Pedestrian detection based on HOG features combined with Adaboost algorithm[J]. Automation Technology and Applications, 2018, 37(7): 89-91. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-ZDHJ201807022.htm
    [7]
    REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]. 2016 IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, USA: IEEE, 2016.
    [8]
    REDMON J, FARHADI A. YOLOv3: an incremental improvement[EB/OL]. (2018-04-08)[2023-06-25]. https://arxiv.org/pdf/1804.02767.pdf.
    [9]
    BOCHKOVSKI A, WANG CY, LIAO H Y. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. (2020-04-23)[2023-06-25]. https://arxiv.org/pdf/2004.10934.pdf.
    [10]
    Ultralytics. Yolov5[CP/OL]. (2022-02-09)[2023-06-25]. https://github.com/ultralytics/yolov5.
    [11]
    LIU W, ANGUELOV D, ERHAN D, et al. Ssd: single shot multibox detector[C]. The European Conference on Computer Vision, Amsterdam, Netherlands: Springer, 2016.
    [12]
    GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbia, USA: IEEE, 2014.
    [13]
    REN S, HE K, GIRSHICK R, et al. Faster r-cnn: towards real-time object detection with region proposal networks[J]. Advances in Neural Information Processing Systems, 2017, 39(6): 1137- 1149.
    [14]
    邵毅明, 屈治华, 邓天民, 等. 基于CapsNet的行人检测方法及评价[J]. 交通运输系统工程与信息, 2019, 19(3): 54-61. https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201903009.htm

    SHAO Y M, QU Z H, DENG T M, et al. Pedestrian detection method and evaluation based on CapsNet[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3): 54-61. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-YSXT201903009.htm
    [15]
    李永上, 马荣贵, 张美月. 改进YOLOv5s+DeepSORT的监控视频车流量统计[J]. 计算机工程与应用, 2022, 58(5): 271-279. https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202205030.htm

    LI Y S, MA R G, ZHANG M Y. Improved surveillance video traffic statistics of YOLOv5s+DeepSORT[J]. Computer Engineering and Applications, 2022, 58(5): 271-279. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSGG202205030.htm
    [16]
    马永杰, 马芸婷, 程时升, 等. 基于改进YOLO v3模型与Deep-SORT算法的道路车辆检测方法[J]. 交通运输工程学报, 2021, 21(2): 222-231. https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202102022.htm

    MA Y J, MA Y T, CHENG S S, et al. Road vehicle detection method based on improved YOLO v3 model and Deep-SORT algorithm[J]. Journal of Transport Information and Safety, 2021, 21(2): 222-231. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JYGC202102022.htm
    [17]
    刘军, 陈岚磊, 李汉冰. 基于类人视觉的多任务交通目标实时检测模型[J]. 汽车工程, 2021, 43(1): 50-58, 67. https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC202101007.htm

    LIU J, CHEN L L, LI H B. Multi-task real-time detection model of traffic targets based on human-like vision[J]. Auto-motive Engineering, 2021, 43(1): 50-58, 67. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-QCGC202101007.htm
    [18]
    TAN M, LE Q. Efficientnetv2: smaller models and faster training[C]. International Conference on Machine Learning, Vienna, Austria: ICML, 2021.
    [19]
    MA N, ZHANG X, SUN J. Funnel activation for visual recognition[C]. Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK: Springer, 2020.
    [20]
    HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA: IEEE, 2021.
    [21]
    XIAO Z, CHEN W, DU L, et al. An improved detection method of traffic prohibition sign for intelligent vehicles based on YOLOV5s[C]. 2023 7th International Conference on Transportation Information and Safety, Xi'an, China: IEEE, 2023.
    [22]
    马浩为, 张笛, 李玉立, 等. 基于改进YOLOv5的雾霾环境下船舶红外图像检测算法[J]. 交通信息与安全, 2023, 41(1): 95-104. doi: 10.3963/j.jssn.1674-4861.2023.01.010

    MA H W, ZHANG D, LI Y L, et al. Ship infrared image detection algorithm in haze environment based on improved YOLOv5[J]. Journal of Transport Information and Safety, 2023, 41(1): 95-104. (in Chinese) doi: 10.3963/j.jssn.1674-4861.2023.01.010
    [23]
    卢鹏, 曹阳, 邹国良, 等. 改进Shufflenetv2_YOLOv5的轻量级SAR图像舰船目标实时检测[J]. 海洋测绘, 2023, 43(1): 58-62, 82. https://www.cnki.com.cn/Article/CJFDTOTAL-HYCH202301013.htm

    LU P, CAO Y, ZOU G L, et al. Improved real-time detection of ship targets in lightweight SAR images using Shufflenetv2_YOLOv5[J]. Ocean Mapping, 2023, 43(1): 58-62, 82. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HYCH202301013.htm
    [24]
    VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]. Annual Conference on Neural Information Processing Systems, Cambridge, USA: MIT Press, 2017.
    [25]
    WOO S, PARK J, LEE J Y, et al. Cbam: convolutional block attention module[C]. The European Conference on Computer Vision, Munich, Germany: ECCV, 2018.
    [26]
    褚文杰. 基于YOLOv5的坦克装甲车辆目标检测关键技术的研究[D]. 北京: 北京交通大学, 2022.

    CHU W J. Research on key technologies for tank and armored vehicle target detection based on YOLOv5[D]. Beijing: Beijing Jiaotong University, 2022. (in Chinese)
    [27]
    YU H, LUO Y, SHU M, et al. Dair-v2x: a large-scale dataset for vehicle-infrastructure cooperative 3d object detection[C]. The IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA: IEEE, 2022.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(3)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (228) PDF downloads(25) Cited by()
    Proportional views
    Related

    Copyright Editorial Office of Transport Information and Safety鄂ICP备05003336号-2

    Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA (430063)

    Tel：027-86580355 Email：jtjsj@vip.163.com

    Supported by: Beijing Renhe Information Technology Co. Ltd  

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return