Abstract
In this work, the YOLOv8 model is adapted to a specific problem in order to increase its performance. Thus, a vision-based system is developed to provide perceptual information to a robot to detect animals in the environment and to be able to perform herding tasks. For this purpose, a dataset is created by selecting animal images from the public AP10K dataset, as well as sheep images acquired by a camera attached to a 4-legged robot. Three different configurations of YOLOv8 are considered: nano, medium and extra-large, trained on the COCO dataset. Its fine-tuning with the animal image dataset shows an improvement in performance achieved not only from the point of view of the robot, but also from the point of view of a drone or a person. The best results are obtained with the YOLOv8 medium configuration when it is trained with the dataset that includes images of the robot’s view.
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Acknowledgements
We gratefully acknowledge the financial support of Grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”. Virginia Riego would like to thank Universidad de León for its funding support for her doctoral studies.
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Riego del Castillo, V., García Sierra, J.F., Sánchez-González, L. (2023). Adapting YOLOv8 as a Vision-Based Animal Detection System to Facilitate Herding. In: García Bringas, P., et al. Hybrid Artificial Intelligent Systems. HAIS 2023. Lecture Notes in Computer Science(), vol 14001. Springer, Cham. https://doi.org/10.1007/978-3-031-40725-3_51
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