Fall Detection for Industrial Setups Using YOLOv8 Variants
This addresses fall detection in industrial settings, but it is incremental as it applies existing YOLOv8 variants with minor enhancements.
The paper tackled industrial fall detection by evaluating YOLOv8 variants with a proposed augmentation pipeline, achieving a mean Average Precision (mAP) of 0.971 at 50% IoU with the YOLOv8m model, which balanced computational efficiency and performance.
This paper presents the development of an industrial fall detection system utilizing YOLOv8 variants, enhanced by our proposed augmentation pipeline to increase dataset variance and improve detection accuracy. Among the models evaluated, the YOLOv8m model, consisting of 25.9 million parameters and 79.1 GFLOPs, demonstrated a respectable balance between computational efficiency and detection performance, achieving a mean Average Precision (mAP) of 0.971 at 50% Intersection over Union (IoU) across both "Fall Detected" and "Human in Motion" categories. Although the YOLOv8l and YOLOv8x models presented higher precision and recall, particularly in fall detection, their higher computational demands and model size make them less suitable for resource-constrained environments.