The group-housed pigs attacking and daily behaviors detection and tracking based on improved YOLOv5s and DeepSORT.

ABSTRACT: Automatic detection and tracking of pig behaviors through video surveillance remain challenges due to farm demanding conditions, e.g., illumination conditions and occlusion of one pig from another. The main goal of this study is to develop a deep learning method based on the improved YOLOv5s and DeepSORT to detect and track the behaviors of pigs, which has the advantages of stability and high accuracy. Firstly, YOLOv5s with the attention mechanism is used for pig detection and behavior recognition. To deal with the missed detection and false detection due to occlusion and overlapping between pigs and pigs, the improved YOLOv5s adopts the Shape-IoU to optimize the bounding box regression loss function, which improves the robustness of the model. Then, the improved DeepSORT model is proposed

SUBMITTER: Cheng T 

PROVIDER: S-EPMC12578152 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature