Design and implementation of safety helmet detection system based on computer vision

Abstract

Production safety is an eternal topic in industrial production. It is very important to detect the wearing condition of workers' safety helmets in construction sites to reduce the occurrence of production accidents. We collected pictures of construction site workers' hard hats, and then preprocessed and labeled them to train and test our models. In this paper, object detection algorithm based on convolutional neural network (YOLOv3) is used to detect whether workers wear safety helmets. Then, the model is improved and optimized by data enhancement, modifying training parameters and increasing training times. Experimental results show that the accuracy of the model is 83.48%, which indicates that the model has good generalization ability and can obtain better real-time recognition and detection effect under the condition of guaranteeing accuracy.