GSCW-YOLO：A Real-Time Lightweight Behavior Recognition Model for Multi- Dairy-Goat

Abstract Livestock behavior serves as a crucial indicator of physiological health. Using deep learning methods to automatically recognize dairy goat behaviors, especially abnormal ones, can aid in the early detection of potential health and environmental issues. This study proposed a multi-scale and lightweight behavior recognition model for dairy goats, known as GSCW-YOLO, which integrates Gaussian Context Transformation (GCT) and the Content-Aware Reassembly of Features upsampling operator (CARAFE). This integration aims to enhance the YOLOv8n model's attention to behavioral features, mitigate complex background interferences and concurrently enhance the model's capability to discern subtle differences among behaviors. Furthermore, the model includes a small target detection layer and optimization of the Wise-IoU loss function to enhance its adaptability in identifying distant small target behaviors and transient abnormal behaviors in surveillance videos. A new dataset called GoatABRD was also established, encompassing four abnormal behaviors (limping, attacking, death and gnawing) and six common behaviors (standing, lying, eating, drinking, scratching and grooming). The experimental results demonstrate that the precision, recall and mean Average Precision (mAP) of the GSCW-YOLO model reached 93.5%, 94.1% and 97.5%, respectively, marking improvements of 3, 3.1 and 2 percentage points over the YOLOv8n model. Moreover, the GSCW-YOLO model has a considerably smaller size of only 5.9MB, with a frame per second (FPS) of 175. Meanwhile, it outperforms popular models like CenterNet, Efficientdet, and other networks in the YOLO series. GSCW-YOLO provides significant technical support for the intelligent management and welfare-focused breeding of dairy goats, thereby facilitating the modernization of the dairy goat industry.