Evaluating the Performance of YOLO-based Hazard Detection Systems: A Quantitative Comparison with Manual Inspection in New York State Workplaces

Abstract

This study quantitatively evaluates the performance of a YOLO-based computer vision system for real-time hazard detection across construction, manufacturing, and healthcare environments in New York State. The analysis compares YOLO-based detection with traditional manual inspection using key performance metrics, including mean average precision (mAP), recall, precision, time-to-detection, and personal protective equipment (PPE) compliance rates. Results indicate that YOLO-based systems significantly outperform manual inspection across all metrics, demonstrating higher detection accuracy, faster response times, and improved compliance monitoring. The findings provide empirical evidence supporting the effectiveness of artificial intelligence–enabled safety systems in enhancing hazard detection performance and advancing proactive safety management practices.