A Lightweight and Efficient Deep Learning Model Based on Improved YOLOv12 for Fall Detection in College Sport Activities

Rapid and accurate fall detection for college students plays a crucial role in enhancing the level of intelligent safety management and improving emergency response mechanisms in universities. Existing models often struggle to achieve a satisfactory balance between efficiency and robustness. To address this, we propose an improved YOLOv12 as the baseline and introduce weighted convolution (WConv) to reduce computational complexity. Additionally, we integrate channel reduction attention (CRA) to strengthen the model’s anti-interference capability. Experimental results demonstrate that the proposed method achieves a mAP@50 of 99.5% and a mAP@[0.5:0.95] of 92.38% on our dataset, with a single-frame inference time of 443 ms. The proposed model strikes a well-balanced and effective trade-off between accuracy and speed, making it particularly suitable for deployment on embedded terminals and offering a reliable monitoring solution for physical education instruction.