YOLOv3在安全帽佩戴检测中的应用研究

针对施工现场环境复杂，基于YOLOv3的安全帽佩戴检测算法存在精度低、鲁棒性差等问题，提出一种改进YOLOv3的安全帽佩戴检测算法。使用K-means算法聚类出先验框，改进了网络输出尺度；并在输出端引入了跳跃连接构成残差模块；同时改进分类损失函数以平衡正负样本、难易样本对模型的影响。为验证该方法的有效性，在NVIDIA GTX1660Ti平台上进行了验证，实验结果表明，改进后的YOLOv3安全帽佩戴检测算法平均准确率提高了4.84%，提升了对被遮挡的目标以及小目标的检测能力，具有较强的鲁棒性。

Application of YOLOv3 in Safety Helmet Wearing Detection

In view of the complex construction site environment and the problems of the YOLOv3 helmet wearing detection algorithm such as low accuracy and poor robustness, an improved YOLOv3 helmet wearing detection algorithm was proposed. The K-means algorithm was used to cluster out anchor boxes, so as to improve the network output scales. The residual block was introduced at the output to form a residual module; at the same time, the classification loss function was improved to balance the influence of positive and negative samples and difficult and easy samples on the model. In order to verify the effectiveness of the method, it was verified on the NVIDIA GTX1660Ti. The experimental results show that the mAP of the improved YOLOv3 helmet wearing detection algorithm increase by 4.84%, the detection ability of occluded targets and small targets is improved, and the model has strong robustness.