基于改进YOLOv5s的安全帽检测算法  

Safety helmet detection algorithm based on improved YOLOv5s

作　　者：赵睿 刘辉 刘沛霖 雷音 李达 ZHAO Rui;LIU Hui;LIU Peilin;LEI Yin;LI Da(School of Physics and Electronic Science,Hunan Normal University,Changsha 410081,China)

机构地区：[1]湖南师范大学物理与电子科学学院,长沙410081

出　　处：《北京航空航天大学学报》2023年第8期2050-2061,共12页Journal of Beijing University of Aeronautics and Astronautics

摘　　要：针对现有安全帽检测算法难以检测小目标、密集目标等缺点,提出一种基于YOLOv5s的安全帽检测改进算法。采用DenseBlock模块来代替主干网络中的切片结构,提升网络的特征提取能力;在网络颈部检测层加入SE-Net通道注意力模块,引导模型更加关注小目标信息的通道特征,以提升对小目标的检测性能;对数据增强方式进行改进,丰富小尺度样本数据集;增加一个检测层以便能更好地学习密集目标的多级特征,从而提高模型应对复杂密集场景的能力。此外,构建一个面向密集目标及远距离小目标的安全帽检测数据集。实验结果表明:所提改进算法比原始YOLOv5s算法平均精确率(mAP@0.5)提升6.57%,比最新的YOLOX-L及PP-YOLOv2算法平均精确率分别提升1.05%与1.21%,在密集场景及小目标场景下具有较强的泛化能力。A YOLOv5s-based helmet detection improvement method is developed in an effort to address the drawbacks of existing safety helmet recognition algorithms,which include difficulty detecting small targets and dense targets.The DenseBlock module is used to replace the slice structure in the backbone network,which improves the feature extraction capability of the network;the SE-Net channel attention module is added to the network neck detection layer,which leads the model to pay more attention to the channel characteristics of small target information,thus improving the performance effect of small objects;the data enhancement method is improved to enrich the small-scale sample data set.A detection layer is added to the model to help it learn multi-level aspects of crowded objects and be better able to handle complicated and dense scenarios.In addition,a helmet detection dataset is constructed for dense targets as well as long-distance small targets.The experimental results show that the improved algorithm improves the average accuracy(mAP@0.5)by 6.57%over the original YOLOv5s algorithm,and it is also increased by 1.05%and 1.21%respectively compared with the latest YOLOX-L and PP-YOLOv2 algorithms and has a strong generalization ability in dense scenes and small target scenes.

关 键 词：安全帽检测 YOLOv5s算法 数据增强 DenseBlock模块 SE-Net注意力模块 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP183[自动化与计算机技术—计算机科学与技术]