Daily behavior recognition of cattle in complex environments based on improved YOLOv8s

Aiming at the problems of inconsistent target scales in the task of daily behavior recognition of cattle in farms due to the complex environment of farms, large changes in illumination, and the uneven distribution of cattle, a method based on the improved YOLOv8s is proposed for the recognition of daily behavior of cattle. By testing on the self-constructed dataset, the average accuracy mean mAP of the improved ELID-YOLOv8s network reaches 85.3%, which is 3.0% higher than the original network model YOLOv8s. Therefore, the method in this paper can significantly improve the recognition accuracy of cattle behavior in different environments, different lighting and multi-scale conditions.