A Novel YOLO Based Safety Helmet Detection in Intelligent Construction Platform

Abstract

Safety is the foremost important issue on the construction site. Wearing a safety helmet is a compulsory issue for every individual in the construction area, which greatly reduces the injuries and deaths. However, though workers are aware of the dangers associated with not wearing safety helmets, many of them may forget to wear helmet at work, which leads to significant potential security issues. To solve this problem, we have developed an automatic computer-vision approach based on Convolutional Neural Network (YOLO) to detect wearing condition. We create a safety helmet image dataset of people working on construction sites. The corresponding images are collected and labeled and are used to train and test our model. The YOLO based model is adopted and parameters are well tuned. The precision of the pro-posed model is 78.3% and the accuracy rate is 20 ms. The results demonstrate that the proposed model is an effective method and comparatively fast for the recognition and localization in real-time helmet detection.