Design and Implementation of a Deep Learning-Based Safety Helmet Compliance Detection System Using the Faster R-CNN Method

Abstract

Workplace accidents remain one of the major issues in industrial environments and are often caused by low compliance with the use of Personal Protective Equipment (PPE), particularly safety helmets. Manual supervision of PPE usage tends to be inefficient and prone to human error. This study aims to develop an intelligent computer-vision-based system capable of automatically and real-time monitoring helmet compliance. The proposed system employs the Faster Region-Convolutional Neural Network (Faster R-CNN) algorithm to detect and classify workers who are wearing and not wearing helmets. The dataset was obtained from CCTV video recordings in industrial areas, which were converted into image frames for training and testing processes. The experimental results show that the system achieved an accuracy of 90% for helmet-wearing workers and 87% for non-helmet-wearing workers during daytime conditions, and 97% and 91% respectively at night. With an average computation time of 0.1 seconds per frame, the system is capable of real-time detection at up to 10 frames per second. These results indicate that the Faster R-CNN method is effective in detecting PPE compliance and has the potential to be implemented as an automated safety-support system in industrial environments.