基于YOLO改进算法的安全帽和口罩佩戴自动同时检测

针对工地、危险区域等场景需要实现同时佩戴安全帽与口罩的自动检测问题，提出一种改进的YOLOv3算法以提高同时检测安全帽和口罩佩戴的准确率。首先，对网络模型中的聚类算法进行优化，使用加权核K-means聚类算法对训练数据集聚类分析，选取更适合小目标检测的Anchor Box，以提高检测的平均精度和速度；然后，优化YOLO网络内部的Darknet特征网络层，将4倍降采样提取的特征图进行2倍上采样，再与2倍降采样进行卷积融合，与4倍降采样、8倍降采样以及16倍降采样一同输送到后续网络中，来达到降低小目标的漏检概率。实验结果表明:改进后的算法同时检测安全帽和口罩佩戴的平均准确率比原算法提高了11.3%。

Automatic Simultaneous Detection of Helmet and Mask Wearing Based on Improved YOLO Algorithm

Aiming at the problem of automatic detection of wearing helmets and masks at the same time in the scene of construction site and dangerous area,an improved YOLOv3 algorithm is proposed to enhance the detection accuracy of helmets and masks.Firstly,the clustering algorithm in network is optimized.The weighted kernel K means clustering algorithm is used to analyze the dataset,so as to select the anchor box more suitable for small targets detection and improve the average accuracy and speed of detection.Secondly,it optimizes the Darknet characteristic network layer in YOLO network.The extracted quadruple down sampling feature map is up sampling once.The double up sampling is fused with the previous double down sampling,and then it is transmitted to the subsequent network together with quadruple down sampling,eightfold down sampling and sixteen times down sampling to reduce the miss detection rate of small targets.Experimental results show that,the average detection accuracy of the improved algorithm is improved by 11.3% when the helmets and masks are worn at the same time.