Abstract: Aiming at the problem of low fall detection accuracy and poor real-time performance in indoor scenes due to the effects of light change, occlusion of the human body form, and changes in the human body posture under special viewpoint, a lightweight improved fall detection algorithm based on YOLOv8, named FDW-YOLO, is proposed. The C2f module in the backbone network is replaced by the FasterNext module, which reduces the computational complexity while retaining the excellent feature extraction capability. According to the characteristics of human falls with large changes in posture, three network structures with dynamically deformable convolutional modules added at different positions in the neck layer are designed, experiments are conducted on a self-made fall dataset for comparison, and ultimately, the YOLOv8-C scheme is selected based on network performance. A bounding box regression loss function WIoU is introduced into the improved network to replace the original CIoU. The experimental results show that compared with YOLOv8n, the FDW-YOLO fall detection algorithm increases mAP@0.5 from 96.5% to 97.9% and mAP@0.5:0.95 from 72.5% to 74.3%, while the number of parameters and computation is only 4.1×106 and 7.3×109, which is in line with the requirements for deployment in low-computing power industrial scenarios.

Key words: object detection, fall, FasterNext, DDConv, WIoU