Helmet Wearing Recognition of Construction Workers Using Convolutional Neural Network

Abstract

One of the most effective ways to prevent construction workers from suffering a head injury is to wear a safety helmet. The use of a computer vision method to detect whether or not construction workers are wearing helmets can improve external construction worker supervision, reducing the number of head injury accidents. A helmet-wearing recognition method based on head recognition is proposed using CNN (convolutional neural network). The area of the head of constructors can be accurately located using crossvalidation of facial feature recognition and head recognition, which solves the problem of determining constructor head position under complex posture. The I-YOLOv3 target detection network is used to detect helmet wear, and the relative position relationship between the helmet and the human body is determined. The findings show that the helmet wearing identification system can be successfully applied to helmet wearing identification work in a complex environment construction site, and it offers a new research perspective and technical method for construction industry information-based safety monitoring.