计量学报2024，Vol.45Issue(7)：974-981,8.DOI:10.3969/j.issn.1000-1158.2024.07.06
一种改进的YOLOv5s航拍车辆检测算法
A Vehicle Detection Algorithm Based on Improved YOLOv5s from the Aerial Perspective
摘要
Abstract
To solve the problem of small vehicle target detection in aerial images,a vehicle detection algorithm based on improved YOLOv5s from the aerial perspective is proposed.The unused shallow feature information is further fused with other deep feature information to compose a new detection layer for small target detection to enhance the detection capability of small targets.The CSP module is combined with the space-to-depth(SPD)module to form the SPD-CSP module,which replaces the downsampling operation of the original network and reduces the loss of practical information of small targets during feature extraction.Furthermore,the efficient channel attention(ECA)module,a channel attention mechanism,is introduced into the Backbone part.To do so,the network will pay more attention to the vital information in the feature graph and reduce the interference of irrelevant information by adaptively adjusting the weight coefficients of different feature channels.The experimental results show that the proposed algorithm improves the mean average precision PmAP0.5by 6.4%on the VisDrone dataset compared to the YOLOv5s network,and the detection speed FPS reaches 65 frames per second,which enables real-time and accurate detection of aerial vehicles.关键词
机器视觉/YOLOv5s/SPD-CSP模块/航拍图像/深度学习/高效通道注意力机制Key words
machine vision/YOLOv5s/SPD-CSP module/aerial image/deep learning/efficient channel attention mechanism分类
通用工业技术引用本文复制引用
张立国,沈明浩,金梅,任婷婷,赵嘉士..一种改进的YOLOv5s航拍车辆检测算法[J].计量学报,2024,45(7):974-981,8.基金项目
国家重点研发计划(2020YFB1711001) （2020YFB1711001）
河北省科学技术研究与发展计划科技支撑计划(20310302D) （20310302D）
