  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.
