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

• Published in: Scientific data 2024-09, Vol.11 (1), p.1-9
• Main Authors: Yuhong Feng, Wen Li, Yuhang Guo, Yifeng Wang, Shengjun Tang, Yichen Yuan, Linlin Shen
• Format: Article
• Language: English
• ISSN: 2052-4463
• DOI: 10.1038/s41597-024-03776-1

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.