Human Body-part Joint Detection (HBJD) has begun to attract research interest in recent years. However, current HBJD methods are usually hard to be applied in real applications due to their complexity. In our work, we concentrate on improving the efficiency of the HBJD and propose the YOLO-HBJD based on YOLOv5-Nano. Specifically, we devise the Feature Holding Down-sampling Module (FHDM) to preserve features of small body parts while reducing computational complexity. In addition, we propose the Context Cross Attention Module (CCAM) to make the YOLO-HBJD focus more on features related to the HBJD. Experiments on the public dataset illustrate that the YOLO-HBJD achieves the best detection performance compared to the comparison methods while reducing parameters and computational complexity by about 90%.
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