|Table of Contents|

CRD-YOLOv5s-based object detection in traffic scenario(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2025年5期
Page:
186-199
Research Field:
交通工程
Publishing date:

Info

Title:
CRD-YOLOv5s-based object detection in traffic scenario
Author(s):
ZHOU Li;  DAI Liang;  YANG Jie;  LING Zhi-kai
(School of Electronics and Control Engineering, Chang'an University, Xi'an 710064, Shaanxi, China)
Keywords:
object detection in traffic scenario;  YOLOv5s;  convolutional block attention module;  receptive field block;  decoupled structure;  smooth intersection over union
PACS:
U495
DOI:
10.19721/j.cnki.1671-8879.2025.05.016
Abstract:
To address the issues of missed and false detections for multi-scale targets in traffic scenarios of complex road environments, an improved model based on YOLOv5s, named CRD-YOLOv5s was proposed. To tackle the problem of missed detections for small targets, a convolutional block attention module(CBAM)was introduced into the backbone network to enhance the feature detection for small objects, and the spatial pyramid pooling module was replaced with a receptive field block(RFB)to strengthen the capability to detect multi objects. The coupled structure of the original head network was substituted with a decoupled structure(DS)to enhance the sufficiency of feature representation and the efficiency of information propagation. The smooth intersection over union(SIoU)loss function was adopted to improve the convergence speed and detection accuracy of the network structure during training. The precision, recall and mean average precision were used to evaluate the model performance, and the test dataset was categorized into pedestrians, cars, traffic lights, and traffic signs. The research results demonstrate that in the car category test, the accuracy of CRD-YOLOv5s is 1.1% higher than YOLOv5s, and the average precision is 0.7% higher than YOLOv5s. For the multi-class detection, the mean average precision of CRD-YOLOv5s reaches 53.3%, exceeding YOLOv5s by 1.3%. CRD-YOLOv5s outperforms the YOLOv5s in multi-object detection, edge object detection and false detection reduction in complex environments. It can significantly improve the detection accuracy from the driver's perspective, effectively reduce the missed and false detections, and is well-suited for object detections in traffic scenarios. While maintaining the competitive performance in single-object detection, it exhibits superior capability in multi-object and complex environment detection. The research results provide an effective mathematical model for practical traffic detection applications and offer a reliable technical support for intelligent traffic detection and traffic safety.7 tabs, 14 figs, 33 refs.

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Last Update: 2025-09-30