基于深度学习的安全帽佩戴检测与跟踪

为了解决传统施工现场安全管理的弊端，减少因施工人员未佩戴安全帽造成的人员伤亡，本文提出一种基于深度学习的安全帽佩戴检测与跟踪方法。首先通过深度学习YOLOv3目标检测网络实现安全帽佩戴检测，进一步运用卡尔曼滤波器和KM算法实现多目标跟踪与计数。复杂施工现场的测试结果表明：网络模型的检测速度可达45 fps，平均精确度为93%，且未佩戴安全帽的查准率和查全率分别为97%和95%，基本能够实现安全帽佩戴情况的实时检测。

Detection and Tracking of Hard Hat Wearing Based on Deep Learning

In order to solve the shortcomings of traditional construction site safety management and reduce casualties caused by construction workers not wearing hard hats, a method for detecting and tracking hard hat wearing based on deep learning is proposed. Firstly, the YOLOv3 target detection network is used to realize the helmet wearing detection, and the Kalman filter and the KM algorithm are used to implement multi-target tracking and counting. The test results at a complex construction site show that the detection speed of the network model can reach 45 fps, with an average accuracy of 93%, and the accuracy and recall rates without a helmet are 97% and 95% respectively. This model basically realizes the real-time detection of the wearing condition of the helmet.