Affiliation:
1. Structural Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt
2. Department of Building and Real Estate, Faculty of Construction and Environment, Hong Kong Polytechnic University, Kowloon 999077, Hong Kong
Abstract
For practitioners and researchers, construction safety is a major concern. The construction industry is among the world’s most dangerous industries, with a high number of accidents and fatalities. Workers in the construction industry are still exposed to safety risks even after conducting risk assessments. The use of personal protective equipment (PPE) is essential to help reduce the risks to laborers and engineers on construction sites. Developments in the field of computer vision and data analytics, especially using deep learning algorithms, have the potential to address this challenge in construction. This study developed several models to enhance the safety compliance of construction workers with respect to PPE. Through the utilization of convolutional neural networks (CNNs) and the application of transfer learning principles, this study builds upon the foundational YOLO-v5 and YOLO-v8 architectures. The resultant model excels in predicting six key categories: person, vest, and four helmet colors. The developed model is validated using a high-quality CHV benchmark dataset from the literature. The dataset is composed of 1330 images and manages to account for a real construction site background, different gestures, varied angles and distances, and multi-PPE. Consequently, the comparison among the ten models of YOLO-v5 (You Only Look Once) and five models of YOLO-v8 showed that YOLO-v5x6’s running speed in analysis was faster than that of YOLO-v5l; however, YOLO-v8m stands out for its higher precision and accuracy. Furthermore, YOLOv8m has the best mean average precision (mAP), with a score of 92.30%, and the best F1 score, at 0.89. Significantly, the attained mAP reflects a substantial 6.64% advancement over previous related research studies. Accordingly, the proposed research has the capability of reducing and preventing construction accidents that can result in death or serious injury.
Reference41 articles.
1. Trends of Fall Accidents in the U.S. Construction Industry;Kang;J. Constr. Eng. Manag.,2017
2. Keller, J.R. (2023, November 27). Construction Accident Statistics. Available online: https://www.2keller.com/library/construction-accident-statistics.cfm.
3. U.S. Bureau of Labor Statistics (BLS) (2023, November 27). National Census of Fatal Occupational Injurie in 2014, Available online: https://www.bls.gov/news.release/archives/cfoi_09172015.pdf.
4. Jeon, J.-H. (2023, November 27). Staff Reporter. 971 S. Korean Workers Died on the Job in 2018, 7 More than Previous Year. Available online: https://www.hani.co.kr/arti/english_edition/e_natio\nal/892709.html.
5. Deep learning-based framework for monitoring wearing personal protective equipment on construction sites;Lee;J. Comput. Des. Eng.,2023