[3Win5] Poster session 3

[3Win5-82] Exploring the use of object detection models for estrus behavior detection in breeding cattle from video

〇Keisuke Kobayashi¹, Teppei Nakano¹, Ryoichi Kasuga³, Hiromi Kusaka², Minoru Sakaguchi², Tetsuji Ogawa¹ (1.Waseda University, 2.Kitasato University, 3.Farmers Support)

Keywords:Object Detection, Video Surveillance, Estrus Behavior Detection, Precision Livestock Farming, Breeding Cattle

We investigated the use of deep learning-based object detection models for identifying estrus behavior in breeding cattle from video.
In livestock reproduction, accurately determining the timing of artificial insemination is critical for efficient operations.
Therefore, it is highly desirable to develop automated techniques to detect estrus-related behaviors, such as mounting, in surveillance camera footage.
This study explores the detection of mounting behavior using existing object detection algorithms.
Specifically, we compared the performance of a lightweight model (YOLO, based on CNN) optimized for computational efficiency and a high-accuracy model (DETR, employing a Transformer architecture).
Our objective was to provide insights into optimal model selection for video-based monitoring of breeding cattle.
Experimental comparisons using farm data demonstrated that the DETR model exhibited superior detection performance for mounting behavior, even under conditions with partial occlusion.