Application of the hybrid machine learning techniques for real-time identification of worker’s personal safety protection equipment

Construction accidents are the most significant contributor to occupational disasters among all industries worldwide. This is due to both the open and dynamic characteristics of construction sites as well as the insufficient quantity and quality of site safety management personnel. The advancement of Artificial Intelligence (AI) deep learning techniques in dynamically identifying the moving objects on-site offers a promising opportunity to improve construction safety. This paper presents the application of the most state-of-the-art AI techniques to identify on-site construction safety hazards in order to prevent risk events for construction workers. The proposed method has been implemented in a real construction project and achieved satisfactory performance with 95% of Recall, 93% of Precision for lab testing, 90% of Correctness and 80% of Cleanness for in-situ testing. It has been concluded that the proposed method has promising potential to assist construction safety management personnel in improving the safety management practices. © 2020, National Taiwan University of Science and Technology. All rights reserved.