面向小目标的YOLOv5s安全帽佩戴检测

本文介绍了一种新的基于YOLOv5s的目标检测方法,旨在弥补当前主流检测方法在小目标安全帽佩戴检测方面的不足,提高检测精度和避免漏检.首先增加了一个小目标检测层,增加对小目标安全帽的检测精度;其次引入ShuffleAttention注意力机制,本文将ShuffleAttention的分组数由原来的64组减少为16组,更加有利于模型对深浅、大小特征的全局提取;最后增加SA-BiFPN网络结构,进行双向的多尺度特征融合,提取更加有效的特征信息.实验表明,和原YOLOv5s算法相比,改善后的算法平均精确率提升了1.7%,达到了92.5%,其中佩戴安全帽和未佩戴安全帽的平均精度分别提升了1.9%和1.4%.本文与其他目标检测算法进行对比测试,实验结果表明SAB-YOLOv5s算法模型仅比原始YOLOv5s算法模型增大了1.5M,小于其他算法模型,提高了目标检测的平均精度,减少了小目标检测中漏检、误检的情况,实现了准确且轻量级的安全帽佩戴检测.

YOLOv5s-based Helmet Wearing Detection for Small Targets

In this study, a new target detection method based on YOLOv5s is introduced to make up for the deficiencies of the current mainstream detection methods in terms of detection precision and missed detection of small target helmet wearing. Firstly, a small target detection layer is added to increase the detection precision of the small target helmet. Secondly, the ShuffleAttention mechanism is introduced. The number of ShuffleAttention groups is reduced from 64 to 16 in this study, which is more conducive to the global extraction of the depth and size of the model. Finally, the SA-BiFPN network structure is added to carry out the bidirectional multi-scale feature fusion to extract more effective feature information. Experiments show that compared with the original YOLOv5s algorithm, the average precision of the improved algorithm is increased by 1.7%, reaching 92.5%. The average precision of the algorithms with and without helmets is increased by 1.9% and 1.4% respectively. The proposed detection algorithm is compared with other target detection algorithms. The experimental results show that the SAB-YOLOv5s algorithm model is only 1.5M larger than the original YOLOv5s algorithm model, which is smaller than other algorithm models. It improves the average precision of target detection, reduces the probability of missing and false detection in small target detection, and achieves accurate and lightweight helmet wearing detection.