TPGR-YOLO: Improving the Traffic Police Gesture Recognition Method of YOLOv11

In open traffic scenarios, gesture recognition for traffic police faces significant challenges due to the small scale of the traffic police and the complex background. To address this, this paper proposes a gesture recognition network based on an improved YOLOv11. This method enhances feature extraction and multi-scale information retention by integrating RFCAConv and C2DA modules into the backbone network. In the Neck part of the network, an edge-enhanced multi-branch fusion strategy is introduced, incorporating target edge information and multi-scale information during the feature fusion phase. Additionally, the combination of WIoU and SlideLoss loss functions optimizes the positioning of bounding boxes and the allocation of sample weights. Experimental validation was conducted on multiple datasets, and the proposed method achieved varying degrees of improvement in all metrics. Experimental results demonstrate that this method can accurately perform the task of recognizing traffic police gestures and exhibits good generalization capabilities for small targets and complex backgrounds.

@article{Qi2025,
  title     = {TPGR-YOLO: Improving the Traffic Police Gesture Recognition Method of YOLOv11},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Xuxing Qi and Cheng Xu and Yuxuan Liu and Nan Ma and Hongzhe Liu},
  doi       = {10.14569/IJACSA.2025.0160243},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160243}
}