Estrus Behavior Recognition and Tracking Method of Dairy Goats Based on Improved YOLOv8s and DeepSort Algorithm

Abstract:

In modern dairy goat farming, accurate recognition and tracking of estrus behavior is crucial for improving breeding efficiency and management standards. Aiming at the lack of accurate and timely behavior analysis and monitoring methods for the estrus of dairy goats in traditional breeding, an estrus behavior recognition and tracking method of dairy goats was proposed. Firstly, the lightweight SimAM attention mechanism was used to enhance the feature extraction capability of YOLOv8s, and a rotating bounding box model (YOLOv8s_obb) and a conventional rectangular box model (YOLOv8s) were used to detect the mounting and the no mounting dairy goats, respectively. Then, combining the detected behavior category information and bounding box parameter information with the DeepSort tracking algorithm, the matching frame count mechanism was introduced into IoU matching process to enhance the correlation between the mounting and the no mounting dairy goats, and accurately determined the dairy goats in estrus, and obtained stable ID, which can ensure the accurate tracking of estrus behavior in successive image frames. The results showed that the AP of the improved model for detecting mounting and non-mounting behaviors in dairy goats was improved by 0.90 percentage points and 2.64 percentage points compared with YOLOv8s_obb and YOLOv8s, respectively. As for estrus goat tracking performance, compared with DeepSort, BotSort, StrongSort and ByteTrack, the proposed method achieved the highest HOTA and IDF1 scores, reaching 72.4% and 80.3% respectively, which can provide strong support for the reproductive management of dairy goats.