A Combined Method for Face and Helmet Detection in Intelligent Construction Site Application

Environment of construction site is becoming more complicated and risky than ever before, due to the rapidly development of society. The traditional site management system is facing the challenges of manual supervision negligence as well as the inflexible attendance system, which cannot guarantee the life safety and legitimate interests of all employees. To address the above problem and reduce the risks, many construction sites utilize intelligent approaches such as effective safety helmet detection and face recognition. This paper propose a hybrid approach combining the popular YOLOv3 and Facenet model to detect the safety helmet wearing of construction workers and help them with attendance checking through camera simultaneously. At first, this method apply YOLOv3 to implement safety helmet detection. Then, Facenet is used to achieve face recognition with face detected by MTCNN model. Finally, combined with the above two modules, helmet detection and personnel information identification can be realized in site supervision system. The experimental results show the effectiveness of the proposed combined approach. As a result, database established by video and image process results can ensure the reasonable salary payment of construction workers, further improving the safety assurance measures and the management efficiency of the construction site.