Abstract: A novel method for verifying the proper use of helmet chin straps during clothing inspections at construction sites is proposed, prioritizing safety in construction environments. As the problem statement, existing helmet-wearing state detection systems often rely on approaches that might not be optimal. This research aims to address limitations in single-view detection and proposes a multi-view deep learning approach for improved accuracy. The proposed method leverages transfer learning for object detection using well-known models such as YOLOv8 and Detectron2. The annotation process for detecting helmet chin straps was conducted using the COCO format with the assistance of Roboflow. Through experimental analysis, the following findings were observed: Using images captured simultaneously from two different angles of the chin strap condition, Detectron2 demonstrated a remarkable ability to accurately determine the state of helmet usage. It could identify conditions such as the chin strap being removed or loosely fastened with 100% accuracy.

Kohei Arai, Kodai Beppu, Yuya Ifuku and Mariko Oda. “Method for Detecting the Appropriateness of Wearing a Helmet Chin Strap at Construction Sites”. International Journal of Advanced Computer Science and Applications (IJACSA) 15.7 (2024). http://dx.doi.org/10.14569/IJACSA.2024.0150717

@article{Arai2024,
title = {Method for Detecting the Appropriateness of Wearing a Helmet Chin Strap at Construction Sites},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150717},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150717},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {7},
author = {Kohei Arai and Kodai Beppu and Yuya Ifuku and Mariko Oda}
}