In response to the safety concerns caused by falls and the limitations of existing object detection models in robustness, generalization and susceptibility to missing fall events, an optimized algorithm for detecting falls in the elderly population was proposed. The improved RepVGG modules was used for replacing the 3×3 convolutional modules of the YOLOv5s algorithm, the loss function was optimized, and the K-means++ clustering algorithm was employed to enhance dataset clustering. Experimental results demonstrate that the proposed algorithm exhibits strong robustness and generalization, achieving an average accuracy improvement of 9%, 8%, 3%, and 12% compared to YOLOv3, YOLOv4, YOLOv5s and CBAM-YOLOv5s models, respectively. The proposed algorithm meets the diverse requirements for fall detection in different real-world scenarios and can be applied in mobile devices or monitoring equipment, making a significant contribution to the field of elderly safety and protection.