PERFORMANCE OF THE YOLOV5 ALGORITHM TO DETECT HUMANS IN THE REAR EXCAVATOR AREA
Abstract
Work involving excavators carries a high risk of accidents that can result in fatalities, making Occupational Safety and Health (OSH) critically important. Most excavator accidents are caused by blind spots at the rear, where the operator's limited field of view increases the risk of hitting nearby objects or workers. Despite safety features such as reverse alarms and rear cameras, these technologies only display real-time video without automatically detecting workers, thus still posing a significant risk. This study aims to develop a human detection system for the rear area of excavators using the YOLOv5 algorithm based on image processing. The system's main features include real-time human detection, distance estimation, and audible warnings if a human is detected within a high-risk distance. The system was tested using three video recordings depicting human objects behind the excavator in different scenarios. Despite the limited number of video samples, the human objects provided sufficient complexity to evaluate the system's effectiveness. The test results showed an average accuracy of 80.5% and an F1-score of 87.78%. These findings indicate that the YOLOv5-based detection system performs well in various video conditions and shows potential effectiveness in real operational situations. Consequently, this system is expected to reduce the risk of work accidents with excavators caused by rear blind spots and improve on-site worker safety. This research contributes to the field of occupational safety by integrating image processing algorithms into the development of heavy equipment safety technology, thereby enhancing worker protection.
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