Safety helmet detection algorithm based on improved YOLOv5s

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. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.