GooseDetect l i o n : A Fully Annotated Dataset for Lion-head Goose Detection in Smart Farms

Abstract

Abstract Large datasets are required to develop Artificial Intelligence (AI) models in AI powered smart farming for reducing farmers’ routine workload, this paper contributes the first large lion-head goose dataset GooseDetect l i o n , which consists of 2,660 images and 98,111 bounding box annotations. The dataset was collected with 6 cameras deployed in a goose farm in Chenghai district of Shantou city, Guangdong province, China. Images sampled from videos collected during July 9 -10 in 2022 were fully annotated by a team of fifty volunteers. Compared with another 6 well known animal datasets in literature, our dataset has higher capacity and density, which provides a challenging detection benchmark for main stream object detectors. Six state-of-the-art object detectors have been selected to be evaluated on the GooseDetect l i o n , which includes one two-stage anchor-based detector, three one-stage anchor-based detectors, as well as two one-stage anchor-free detectors. The results suggest that the one-stage anchor-based detector You Only Look Once version 5 (YOLO v5) achieves the best overall performance in terms of detection precision, model size and inference efficiency.