Innovations in safety management for construction sites: the role of deep learning and computer vision techniques
Amr A. Mohy, Hesham Bassioni, Elbadr O. Elgendi, Tarek M. Hassan
Abstract
Purpose The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) techniques for improving safety on construction sites. It provides an overview of the current state of research in the field of construction site safety (CSS) management using these technologies. Specifically, the study focuses on identifying hazards and monitoring the usage of personal protective equipment (PPE) on construction sites. The findings highlight the potential of computer vision and DL to enhance safety management in the construction industry. Design/methodology/approach The study involves a scientometric analysis of the current direction for using computer vision and DL for CSS management. The analysis reviews relevant studies, their methods, results and limitations, providing insights into the state of research in this area. Findings The study finds that computer vision and DL techniques can be effective for enhancing safety management in the construction industry. The potential of these technologies is specifically highlighted for identifying hazards and monitoring PPE usage on construction sites. The findings suggest that the use of these technologies can significantly reduce accidents and injuries on construction sites. Originality/value This study provides valuable insights into the potential of computer vision and DL techniques for improving safety management in the construction industry. The findings can help construction companies adopt innovative technologies to reduce the number of accidents and injuries on construction sites. The study also identifies areas for future research in this field, highlighting the need for further investigation into the use of these technologies for CSS management.