Object Detection for Hazardous Material Vehicles Based on Improved YOLOv5 Algorithm

被引:10
|
作者
Zhu, Pengcheng [1 ]
Chen, Bolun [1 ,2 ]
Liu, Bushi [1 ]
Qi, Zifan [1 ]
Wang, Shanshan [1 ]
Wang, Ling [1 ]
机构
[1] Huaiyin Inst Technol, Fac Comp & Software Engn, Huaian 223003, Peoples R China
[2] Univ Fribourg, Dept Phys, CH-1700 Fribourg, Switzerland
基金
中国国家自然科学基金;
关键词
hazardous material vehicles; object detection; YOLOv5; attention mechanism; NETWORK;
D O I
10.3390/electronics12051257
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Hazardous material vehicles are a non-negligible mobile source of danger in transport and pose a significant safety risk. At present, the current detection technology is well developed, but it also faces a series of challenges such as a significant amount of computational effort and unsatisfactory accuracy. To address these issues, this paper proposes a method based on YOLOv5 to improve the detection accuracy of hazardous material vehicles. The method introduces an attention module in the YOLOv5 backbone network as well as the neck network to achieve the purpose of extracting better features by assigning different weights to different parts of the feature map to suppress non-critical information. In order to enhance the fusion capability of the model under different sized feature maps, the SPPF (Spatial Pyramid Pooling-Fast) layer in the network is replaced by the SPPCSPC (Spatial Pyramid Pooling Cross Stage Partial Conv) layer. In addition, the bounding box loss function was replaced with the SIoU loss function in order to effectively speed up the bounding box regression and enhance the localization accuracy of the model. Experiments on the dataset show that the improved model has effectively improved the detection accuracy of hazardous chemical vehicles compared with the original model. Our model is of great significance for achieving traffic accident monitoring and effective emergency rescue.
引用
收藏
页数:16
相关论文
共 50 条
  • [41] Lightweight Fire Detection Algorithm Based on Improved YOLOv5
    Zhang, Dawei
    Chen, Yutang
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2024, 15 (06) : 809 - 816
  • [42] Bearing defect detection based on the improved YOLOv5 algorithm
    Li, Kangning
    Jiao, Peigang
    Ding, Jiaming
    Du, Weibo
    PLOS ONE, 2024, 19 (10):
  • [43] Lightweight UAV Detection Algorithm Based on Improved YOLOv5
    Peng Y.
    Tu X.
    Yang Q.
    Li R.
    Hunan Daxue Xuebao/Journal of Hunan University Natural Sciences, 2023, 50 (12): : 28 - 38
  • [44] Research on pedestrian object detection algorithm in urban road scenes based on improved YOLOv5
    Liu Z.
    Wang X.
    Journal of Intelligent and Fuzzy Systems, 2024, 1 (01):
  • [45] Small object detection in UAV image based on improved YOLOv5
    Zhang, Jian
    Wan, Guoyang
    Jiang, Ming
    Lu, Guifu
    Tao, Xiuwen
    Huang, Zhiyuan
    SYSTEMS SCIENCE & CONTROL ENGINEERING, 2023, 11 (01)
  • [46] Drone-View Object Detection Based on the Improved YOLOv5
    Yang, Yanzhao
    2022 IEEE INTERNATIONAL CONFERENCE ON ELECTRICAL ENGINEERING, BIG DATA AND ALGORITHMS (EEBDA), 2022, : 612 - 617
  • [47] Object Detection for Construction Waste Based on an Improved YOLOv5 Model
    Zhou, Qinghui
    Liu, Haoshi
    Qiu, Yuhang
    Zheng, Wuchao
    SUSTAINABILITY, 2023, 15 (01)
  • [48] Object Detection Method for Grasping Robot Based on Improved YOLOv5
    Song, Qisong
    Li, Shaobo
    Bai, Qiang
    Yang, Jing
    Zhang, Xingxing
    Li, Zhiang
    Duan, Zhongjing
    MICROMACHINES, 2021, 12 (11)
  • [49] Rotated Aerial Object Detection Based on Improved YOLOv5 Method
    Fan, Yali
    Chen, Junhai
    Ma, Zhaowei
    PROCEEDINGS OF 2022 INTERNATIONAL CONFERENCE ON AUTONOMOUS UNMANNED SYSTEMS, ICAUS 2022, 2023, 1010 : 847 - 856
  • [50] HIC-YOLOv5: Improved YOLOv5 For Small Object Detection
    Tang, Shiyi
    Zhang, Shu
    Fang, Yini
    2024 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ICRA 2024, 2024, : 6614 - 6619