YOLO-HBJD: towards efficient human body-part joint detection

Yicheng Tong; Boyuan Meng; Congling Tian; Liping Wang; Yan Liu; Guosen Lyu; Guan Yue; Deya Zhu; Liujie Li

doi:10.1117/12.3029256

22 May 2024 YOLO-HBJD: towards efficient human body-part joint detection

Yicheng Tong, Boyuan Meng, Congling Tian, Liping Wang, Yan Liu, Guosen Lyu, Guan Yue, Deya Zhu, Liujie Li

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131763H (2024) https://doi.org/10.1117/12.3029256
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

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%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yicheng Tong, Boyuan Meng, Congling Tian, Liping Wang, Yan Liu, Guosen Lyu, Guan Yue, Deya Zhu, and Liujie Li "YOLO-HBJD: towards efficient human body-part joint detection", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131763H (22 May 2024); https://doi.org/10.1117/12.3029256

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KEYWORDS

Object detection

Sensors

Ablation

Education and training

Convolution

Semantics

Facial recognition systems

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