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Chapter iSafe Welding System: Computer Vision-Based Monitoring System for Safe Welding Work
- Publication Year :
- 2023
- Publisher :
- Florence: Firenze University Press, 2023.
-
Abstract
- The construction industry faces significant challenges, including a high prevalence of occupational incidents, often involving fires, explosions, and burn-related accidents due to worker non-compliance with safety protocols. Adherence to safety guidelines and proper utilization of safety equipment are critical to preventing such incidents and safeguarding workers in hazardous work environments. Consequently, a monitoring system tailored for construction safety during welding operations becomes imperative to mitigate the risk of fire accidents. This paper conducts a brief analysis of OSHA rules pertaining to welding work and introduces the iSafe Welding system, an advanced real-time safety monitoring and compliance enforcement solution designed specifically for construction site welding operations. Harnessing the real-time object detection algorithm YOLOv7 in conjunction with rule-based scene classification, the system excels in identifying potential safety violations. Rigorous evaluation, encompassing precision, recall, mean Average Precision (mAP), accuracy, and the F1-Score, sheds light on its strengths and areas for improvement. The system showcases robust performance in rule-based scene classification, achieving high accuracy, precision, and recall rates. Notably, the iSafe Welding system demonstrates a formidable potential for enhancing construction site safety and regulatory compliance. Ongoing enhancements, including dataset expansion and model refinement, underscore its commitment to real-world deployment and its strength in ensuring worker safety
Details
- Language :
- English
- ISBN :
- 979-1-221-50289-3
- ISSN :
- 27045846
- ISBNs :
- 9791221502893
- Database :
- OAPEN Library
- Notes :
- ONIX_20240402_9791221502893_35, , https://books.fupress.com/doi/capitoli/979-12-215-0289-3_66
- Publication Type :
- eBook
- Accession number :
- edsoap.20.500.12657.89066
- Document Type :
- chapter
- Full Text :
- https://doi.org/10.36253/979-12-215-0289-3.66