HRDS: A High-Dimensional Lightweight Keypoint Detection Network Enhancing HRNet with Dim-Channel and Space Gate Attention Using Kolmogorov-Arnold Networks
Xinran Wang, Guoliang Li, Feng Liu · Electronics · 2025
Animal keypoint detection holds significant applications in fields such as biological behavior research and animal health monitoring. Although related research has reached a relatively mature stage of human keypoint detection, it still faces numerous challenges in the realm of animal keypoint detection. Firstly, there is a scarcity of keypoint detection datasets related to animals in public datasets. Secondly, existing solutions have adopted large-scale deep learning models to achieve higher accuracy, but these models are costly and difficult to widely promote within the industry. On the other hand, small-scale, low-cost detection models that are used to reduce costs suffer from insufficient accuracy and cannot meet the needs of industrial production. Therefore, designing and implementing a lightweight and high-accuracy animal keypoint detection model to meet industry needs has significant theoretical and practical importance. Addressing the aforementioned issues, this thesis proposes a lightweight animal keypoint detection method, HRDS, which maintains high accuracy while significantly reducing model complexity. Firstly, by removing the fourth stage from the HRNet architecture, the number of parameters and the computational complexity of the model are successfully reduced. Secondly, to enhance the model’s performance and robustness, a new attention mechanism module, DS, is designed. This module effectively counterbalances the loss of accuracy due to the significant reduction in the parameter count and helps to strengthen the model’s keypoint detection capability in complex scenarios. Experiments were performed on the AP-10K dataset, and the results indicated that the HRDS method achieves an accuracy rate of 70.34%, with a 73.05% reduction in the number of parameters compared to HRNet and only a 2.64% decrease in accuracy, maintaining high precision. The inference time of HRDS reaches 26.58 ms, with an inference speed of 37.62 FPS, and its inference time is only 87.3% of that of HRNet. This provides a new solution for applications in resource-constrained environments.