Volume 42 Issue 1
Feb.  2024
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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.

     

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