A Target Detection and Evaluation Method for Safety Protection Equipment

Abstract

Abstract The existing safety detection methods are not sufficient to achieve real-time accurate detection, and cannot perform real-time safety assessment. Taking the construction site as an example, this paper proposes a safety equipment wear detection method based on the joint constraint of linear distance and angle, and proposes the idea of establishing a knowledge database of safety protection equipment wear behavior, and proposes a safety factor evaluation model. In this paper, convolutional neural networks are used to identify the construction personnel and safety equipment in two basic poses, and the linear distance and deflection angle of the regional center point coordinates are used to jointly determine the wearing status of the safety equipment. The generated result matches the safety information knowledge base to obtain the final test results. We obtained the real-time safety factor of the construction personnel by correlating the detection results with the actual scene. Finally, the method used in the article is summarized and prospected.