Smart Surveillance for Fall Detection with YOLOV10 in Unstructured Outdoor Settings

Abstract - Falls are one of the most common and dangerous problems in industrial areas and open spaces, often causing serious injuries and safety issues. It's hard to detect falls quickly and exactly, especially when using devices that have limited power and processing ability. To solve this, this paper introduces YOLOv10-Fall, a deep learning system designed to detect falls in real time in outdoor areas with no clear structure. The system uses the YOLOv10 model, which is better at recognizing shapes and context while still being lightweight for quicker processing. The model has a simpler main part and a better detection part to improve accuracy and reduce the amount of processing needed. This means the system can spot falls accurately even in tough situations with changing light and busy backgrounds. Testing on standard fall detection datasets shows YOLOv10-Fall has better accuracy, higher mean Average Precision (mAP), and faster processing speed than earlier models like YOLOv7-tiny. These improvements make it a good choice for real-time smart security and safety monitoring systems. Key Words: Fall Detection, YOLOv10, Deep Learning, Computer Vision, Smart Surveillance, Object Detection