SMART SURVEILLANCE FOR FALL DETECTION WITH YOLOV10 IN UNSTRUCTURED OUTDOOR SETTINGS

Abstract

Falls are one of the most frequent and hazardous incidents occurring in industrial and open environments, posing serious threats to individual safety. Prompt and accurate fall detection remains a critical challenge, especially when deploying models on edge or low-power devices. To address these issues, this paper proposes YOLOv10- Fall, a robust and efficient deep learningbased framework for real-time fall detection in open spaces. Leveraging the cutting-edge YOLOv10 architecture, the proposed method enhances detection performance through improved spatial and contextual feature representation while significantly reducing computational overhead. YOLOv10-Fall integrates a lightweight yet powerful backbone and a streamlined detection head to facilitate faster inference and higher precision in complex scenes. Experimental evaluations on benchmark fall datasets demonstrate that YOLOv10-Fall achieves superior detection accuracy and mAP compared to previous models like YOLOv7- tiny, while also offering improved inference speed and reduced parameter complexity. These advancements make YOLOv10-Fall a practical and scalable solution for deployment in real-world surveillance and safety monitoring systems.