基于改进YOLOv5的车辆检测方法  

VEHICLE DETECTION METHOD BASED ON IMPROVED YOLOV5

作　　者：梁秀满 赵恒斌 邵彭娟 高绍品 Liang Xiuman;Zhao Hengbin;Shao Pengjuan;Gao Shaopin(School of Electrical Engineering,North China University of Science and Technology,Tangshan 063210,Hebei,China)

机构地区：[1]华北理工大学电气工程学院,河北唐山063210

出　　处：《计算机应用与软件》2025年第4期217-222,共6页Computer Applications and Software

摘　　要：为了促进自动驾驶技术的发展,针对现有车辆检测算法对小尺寸目标的检测效果差、精度较低的现状,提出一种基于改进YOLOv5的车辆检测算法QF-YOLOv5。在YOLOv5结构上,增加一层小尺寸特征融合检测层,提高小目标的检测能力。引入注意力机制,使网络聚焦有效特征,抑制干扰特征,提高了算法检测能力,并采用深度可分离卷积来降低网络计算量。使用了Mini Batch K-Means聚类算法,加快网络收敛。利用Quality Focal loss损失函数,使网络对连续数值有监督能力。实验结果表明:该算法在检测准确性和实时性上,都有一定程度的提高。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 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]