GMS-YOLO11n: A Sheep Detection Model for Challenging Fixed-View Farm Conditions Integrating Spatially Gated Structural Enhancement and Multi-Scale Attention
TL;DR
GMS-YOLO11n, a new computer vision model for automatic sheep detection, improves the original YOLO11n model by strengthening important visual features, such as sheep edges, body contours, and local textures, while combining information from different image scales.
Abstract
Simple Summary Sheep detection is an important part of intelligent farm management because it supports automatic counting, individual tracking, behavior observation, and health monitoring. However, accurately detecting sheep can be difficult when animals gather closely together, partially block one another, appear at different distances from the camera, or are recorded under poor lighting and complex backgrounds. To address these challenges, we developed GMS-YOLO11n, a new computer vision model for automatic sheep detection. Our approach improves the original YOLO11n model by strengthening important visual features, such as sheep edges, body contours, and local textures, while combining information from different image scales. In the current within-farm evaluation, the proposed model reduced missed detections and improved bounding-box localization, particularly in the nighttime low-light and obvious-occlusion subsets. These findings are limited to the fixed-view surveillance conditions represented in the present dataset and should not be interpreted as evidence of general performance across different farms, breeds, camera devices, or viewpoints. External validation is required before the model can be considered suitable for broader farm deployment.