Real-time Identification of Worker’s Personal Safety Equipment with Hybrid Machine Learning Techniques

This paper presents an application of the hybrid Machine Learning (ML) techniques to real-time detection of unsafe personal safety equipment (e.g., helmet and safety vest) of construction workers on site, so that the unsafe behaviors can be corrected timely to reduce safety risks. Three different Convolutional Neural Network (CNN) based Deep Learning (DL) techniques were adopted for worker position locating, object classification, and subtle feature detection, including Faster R-CNN, YOLO and DenseNet. The lab testing showed high detectability with the Recall of 95% and the Precision of 90%. In in-situ implementation of a real-world construction site, a moderately acceptable detectability was achieved, with the Cleanness of 85% and Correctness of 80%. It is concluded that the proposed method quotes profound potentials to enhance the current safety management practice of construction site.