YOLO-fall: a YOLO-based fall detection model with high precision, shrunk size, and low latency

Abstract According to recent research statistics, falling has become an important factor affecting the health and safety of the elderly. To reduce the computational cost of hardware and meet the demand for real-time fall detection, we propose a lightweight fall detection network called YOLO-fall oriented for mobile and small edge computing devices. We have made the following improvements based on you only look once (YOLO). First, the backbone network is designed to be lightweight. Then, the convolution module is reparameterized and the C3 structure is improved to ensure the balance between speed and accuracy. Finally, a 5 × 5 depth convolution is added to the detection head to improve the detection ability of large targets. The proposed YOLO-fall is trained and validated on the E-FPDS public dataset and achieves a 78.4% mean average precision (mAP) with 2.45 M parameters and 12.2 GFLOPs. Compared with YOLOv5s, YOLO-fall has a 6.1% improvement in mAP and a 65.1% reduction in parameters. Although Yolov9s has a higher mAP of 82.9%, YOLO-fall reduces the parameters and calculation quantities by 74.8 and 69.2%, respectively. Therefore, the proposed YOLO-fall has the potential to accurately perform real-time fall detection on mobile and small edge computing devices.