DOI QR코드

DOI QR Code

Performance Analysis of Algorithms Applying YOLOv8 and OC-SORT for Livestock Behavior Analysis

  • Doyoon Jung (Department of Computer Engineering, Honam University) ;
  • Sukhun Kim (Department of Computer Engineering, Honam University) ;
  • Namho Kim (Department of Computer Engineering, Honam University)
  • 투고 : 2024.09.20
  • 심사 : 2024.10.02
  • 발행 : 2024.11.30

초록

This research develops a smart livestock monitoring system leveraging artificial intelligence with YOLOv8 and OC-SORT technologies to precisely monitor and analyze cow behavior, enhancing detection and tracking capabilities in complex environments. It delves into cows' movement speed and acceleration to uncover behavior patterns and health status, focusing on estrus-related behaviors for optimal breeding strategies. The study identifies changes in activity, social interactions, and mating behaviors as crucial estrus indicators, contributing significantly to livestock management innovations. By offering methods for visual behavior analysis representation, it simplifies the interpretation of findings, advancing livestock monitoring technology. This work not only contributes to smarter livestock management by providing an AI-driven cow behavior tracking model but also opens new avenues for research and efficiency improvements in the field.

키워드

과제정보

This research was supported by "Regional Innovation Strategy (RIS)" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE)(2021RIS-002).

참고문헌

  1. Cao, J., Pang, J., Weng, X., Khirodkar, R., Kitani, K., "Observation-centric sort: Rethinking sort for robust multiobject tracking", In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 9686-9696, 2023. DOI: https://doi.org/10.1109/CVPR52729.2023.00934
  2. Vasseur, E., "Animal behavior and well-being symposium: Optimizing outcome measures of welfare in dairy cattle assessment", Journal of Animal Science, 95(3), pp. 1365-1371, 2017. DOI: https://doi.org/10.2527/jas.2016.0880
  3. Wang, J., Zhang, H., Zhao, K., Liu, G., "Cow movement behavior classification based on optimal binary decisiontree classification model", Trans. Chin. Soc. Agric. Eng, 34, pp. 202-210, 2017. DOI: https://dx.doi.org/10.11975/j.issn.1002-6819.2018.18.025
  4. Nasirahmadi, A., Hensel, O., Edwards, S.A., & Sturm, B., "A new approach for categorizing pig lying behaviour based on a Delaunay triangulation method", Animal, 11(1), pp. 131-139, 2017. DOI: https://doi.org/10.1017/S1751731116001208
  5. Peng, Y., Kondo, N., Fujiura, T., Suzuki, T., Yoshioka, H., & Itoyama, E., "Classification of multiple cattle behavior patterns using a recurrent neural network with long short-term memory and inertial measurement units", Computers and electronics in agriculture, 157, pp. 247-253, 2019. DOI: https://doi.org/10.1016/j.compag.2018.12.023
  6. Nasirahmadi, A., Edwards, S.A., Sturm, B., "Implementation of machine vision for detecting behaviour of cattle and pigs", Livestock Science, 202, pp. 25-38, 2017. DOI: https://doi.org/10.1016/j.livsci.2017.05.014
  7. Sun, Y., Yue, K., Wenxi, L. I., Yao, E., Liu, X., Yang, L. I., & Zhang, Y., "Application of image information technology in dairy cow production", Chinese Journal of Animal Nutrition, 30(5), pp. 1626-1632, 2018.
  8. C, Lyu., W, Zhang., H, Huang., Y, Zhou., Y, Wang., Y, Liu., S, Zhang., K, Chen., "RTMDet: An Empirical Study of Designing Real-Time Object Detectors", arXiv preprint arXiv:2308.05480, 2022. DOI: https://doi.org/10.48550/arXiv.2212.07784
  9. Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M., "YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors", arXiv preprint arXiv:2207.02696, 2023. DOI: https://doi.org/10.48550/arXiv.2207.02696