Deformable convolution and coordinate attention for fast cattle detection

Cattle detection is an important task in precision livestock farming, but it remains challenging due to the varying appearance and poses of cattle in different scenarios. In this paper, we propose a novel approach for fast cattle detection using deformable convolution and coordinate attention within YOLOv8, a SOTA object detection model. Our proposed method enhances the YOLOv8 architecture by introducing deformable convolution to capture more fine-grained spatial information and coordinate attention to emphasize important features in the detection process. We evaluate our method on a cattle dataset collected in a cattle farm and achieve superior performance compared to the baseline YOLOv8 and several SOTA object detection models. Specifically, our approach achieves a mean average precision (mAP) of 72.9% at 62.5 frames per second (FPS), which demonstrates its effectiveness and efficiency for fast cattle detection. By deploying our method on the farm’s monitoring computer, our proposed approach has the potential to facilitate the development of automated cattle monitoring systems for improving animal welfare and farm management.