GooseDetect: A Fully Annotated Dataset for Lion-head Goose Detection in Smart Farms.
Journal:
Scientific data
PMID:
39244605
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, 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, 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.