YOLOv5s model with attention modules.

<div><p>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 impr...

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Bibliographic Details
Main Author:	Tianyu Cheng (5075000) (author)
Other Authors:	Fujie Sun (22537638) (author), Liang Mao (200719) (author), Haoxuan Ou (22537641) (author), Shuqin Tu (22537644) (author), Fang Yuan (96264) (author), Hairan Yang (22537647) (author)
Published:	2025
Subjects:	Ecology Sociology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified experimental results show farm demanding conditions false detection due accuracy improvement compared housed pigs attacking improved yolov5s adopts daily behaviors detection deepsort </ p improved deepsort model improved yolov5s pig detection missed detection high accuracy density conditions yolov5s model test videos special dataset pig behaviors one pig main goal experiments demonstrate different lighting comparison test behavior recognition attention mechanism 3 %,
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YOLOv5s model with attention modules.

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