Improved YOLOv11pose for Posture Estimation of Xinjiang Bactrian Camels

Automatic pose estimation of camels is crucial for long-term health monitoring in animal husbandry. There is currently less research on camels, and our study has certain practical application value in actual camel farms. Due to the high similarity of camels, this has brought us a huge challenge in pose estimation. This study proposes YOLOv11pose-Camel, a pose estimation algorithm tailored for Bactrian camels. The algorithm enhances feature extraction with a lightweight channel attention mechanism (ECA) and improves detection accuracy through an efficient multi-scale pooling structure (SimSPPF). Additionally, C3k2 modules in the neck are replaced with dynamic convolution blocks (DECA-blocks) to strengthen global feature extraction. We collected a diverse dataset of Bactrian camel images with farm staff assistance and applied data augmentation. The optimized YOLOv11pose model achieved 94.5% accuracy and 94.1% mAP@0.5 on the Xinjiang Bactrian camel dataset, outperforming the baseline by 2.1% and 2.2%, respectively. The model also maintains a good balance between detection speed and efficiency, demonstrating its potential for practical applications in animal husbandry.

@article{Liu2024,
  title     = {Improved YOLOv11pose for Posture Estimation of Xinjiang Bactrian Camels},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {12},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Lei Liu and Alifu Kurban and Yi Liu},
  doi       = {10.14569/IJACSA.2024.0151239},
  url       = {https://doi.org/10.14569/IJACSA.2024.0151239}
}