计算机应用与软件2025，Vol.42Issue(4)：217-222,6.DOI:10.3969/j.issn.1000-386x.2025.04.031
基于改进YOLOv5的车辆检测方法
VEHICLE DETECTION METHOD BASED ON IMPROVED YOLOV5
梁秀满 1赵恒斌 1邵彭娟 1高绍品1
作者信息
- 1. 华北理工大学电气工程学院 河北唐山 063210
- 折叠
摘要
Abstract
To promote the development of autonomous driving technology,this study addresses the poor detection performance and low accuracy of existing vehicle detection algorithms for small-sized targets by proposing QF-YOLOv5,an improved YOLOv5-based vehicle detection algorithm.Building upon the YOLOv5 architecture,the following enhancements are introduced:An additional small-scale feature fusion detection layer is incorporated to enhance the detection capability for small targets.An attention mechanism is integrated to guide the network to focus on effective features while suppressing irrelevant ones,thereby improving detection performance.Depthwise separable convolution is adopted to reduce computational complexity.The Mini Batch K-Means clustering algorithm is employed to accelerate network convergence.The Quality Focal loss function is utilized to enable supervised learning for continuous numerical predictions.Experimental results demonstrate that the proposed algorithm achieves improvements in both detection accuracy and real-time performance.关键词
车辆检测/YOLOv5/目标检测/深度可分离卷积/注意力机制Key words
Vehicle detection/YOLOv5/Object detection/Depthwise separable convolution/Attention mechanism分类
信息技术与安全科学引用本文复制引用
梁秀满,赵恒斌,邵彭娟,高绍品..基于改进YOLOv5的车辆检测方法[J].计算机应用与软件,2025,42(4):217-222,6.
