Human fall detection in industrial environment using YOLOv8 with transfer learning

Ensuring worker safety in industrial environments, particularly in high-risk areas like pipe production lines, necessitates reliable fall detection systems. This research introduces a novel two-step model employing the YOLOv8 framework for real-time fall detection. Initially, a YOLOv8 model is trained to detect the presence of persons within the production line using annotated video data. Subsequently, a second YOLOv8 model is trained to classify these detected instances into 'fall' and 'no fall' categories. To optimize real-time performance, a frame-skipping technique is integrated, reducing computational demands while preserving detection accuracy. Furthermore, the system employs a Region of Interest (ROI) approach, focusing on specific hazardous zones where falls are more likely to occur, thereby enhancing the model's efficiency and relevance. Experimental results indicate that the proposed method achieves high precision and recall and F1 in fall detection, demonstrating its robustness and effectiveness.

Human fall detection in industrial environment using YOLOv8 with transfer learning Keywords:

component , Fall Detection , YOLOv8 , Industrial Safety , Real-Time Monitoring , Object detection