Real-time detection model of highway vehicle based on YOLOv5s

LIU Yuan-feng; JI Hai-jun; LIU Li-bo

doi:10.37188/CJLCD.2022-0026

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Real-time detection model of highway vehicle based on YOLOv5s

Image Processing | 更新时间：2022-12-07

- Real-time detection model of highway vehicle based on YOLOv5s
- Chinese Journal of Liquid Crystals and Displays Vol. 37, Issue 9, Pages: 1228-1241(2022)
- 作者机构：
  
  1.宁夏大学信息工程学院，宁夏银川 750021
  2.宁夏路网监测与应急处置中心，宁夏银川 750021
- 作者简介：
- 基金信息：
  
  Key Research and Development Program of Ningxia(2021BEG03024)
- DOI：10.37188/CJLCD.2022-0026
  CLC： U495;TP391
- Received：24 January 2022，
  
  Revised：16 March 2022，
  
  Published：05 September 2022
- 稿件说明：
移动端阅览
LIU Yuan-feng, JI Hai-jun, LIU Li-bo. Real-time detection model of highway vehicle based on YOLOv5s[J]. Chinese journal of liquid crystals and displays, 2022, 37(9): 1228-1241.
DOI：

LIU Yuan-feng, JI Hai-jun, LIU Li-bo. Real-time detection model of highway vehicle based on YOLOv5s[J]. Chinese journal of liquid crystals and displays, 2022, 37(9): 1228-1241. DOI： 10.37188/CJLCD.2022-0026.

摘要

针对高速复杂环境下YOLOv5s算法细节特征学习能力弱、冗余信息过多、关键特征融合不足导致车辆目标检测精度低的问题，本文提出一种基于YOLOv5s的高速公路车辆实时检测模型（YOLOv5s-CRCP）。首先，在残差单元中嵌入卷积注意力模块，强化学习细节特征，抑制冗余信息干扰；然后，将卷积注意力融入金字塔网络中用以区分不同重要信息，加强关键特征融合。在构建的宁夏高速公路车辆数据集上进行实验，平均检测精度达到91.2%，高出原算法4.1%。实验结果表明，相较于YOLOv5s和主流实时车辆目标检测算法，本文方法具有更好的检测性能。

Abstract

Aiming at the problems that the YOLOv5s algorithm has weak detailed feature learning ability， excessive redundant information， and insufficient key feature fusion in complex highway environments leads to low accuracy of vehicle target detection， a real-time detection model of highway vehicle is proposed based on YOLOv5s （YOLOv5s-CRCP）. Firstly， convolutional attention module is embeded in the residual unit to strengthen the learning of detailed features and suppress the interference of redundant information. Secondly， convolutional attention is integrated into pyramid network to distinguish different important information and strengthen the fusion of key features. Experiments are conducted on the constructed Ningxia highway vehicle data set， and the average detection accuracy reaches to 91.2%， which is 4.1% higher than that of original algorithm. Experimental results show that the proposed method has better detection performance in comparison with YOLOv5s and the mainstream real-time vehicle target detection algorithms.

关键词

Keywords

references

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