An Improved YOLOv8 Method for Measuring the Body Size of Xinjiang Bactrian Camels

Camel body size measurement has initially been applied in livestock production. However, current methods suffer from low measurement accuracy due to detection box localization loss and occlusions. This study proposes an effective algorithm, Camel-YOLOv8, specifically designed for detecting Xinjiang Bactrian camels and calculating their body sizes. By integrating the Selective Kernel Networks (SKAttention) mechanism and an enhanced Asymptotic Feature Pyramid Network structure (AFPN-beta), the algorithm successfully captures the body characteristics of Bactrian camels in natural environments and converts these into precise size data. We have developed a Xinjiang Bactrian camel body size measurement dataset and applied the enhanced YOLOv8 model for accurate classification and detection. By extracting key point pixel values and applying Zhang Zhengyou’s calibration method, the coordinate system data is converted into accurate body size measurements. The Camel-YOLOv8 achieves a detection accuracy of 76.4% on the Xinjiang Bactrian camel dataset, marking a 3.7% improvement over the baseline model. In terms of body size calculation, the average relative errors for height and chest circumference are -3.39% and 4.1%, respectively, demonstrating high measurement precision. The algorithm not only maintains high detection accuracy but also achieves a reasonable balance between detection speed and efficiency, providing an effective solution for rapid acquisition of camel body size information.

@article{Peng2024,
  title     = {An Improved YOLOv8 Method for Measuring the Body Size of Xinjiang Bactrian Camels},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {8},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Yue Peng and Alifu Kurban and Mengmei Sang},
  doi       = {10.14569/IJACSA.2024.0150857},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150857}
}