The College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China.
The School of Architecture and Urban Planning, Research Institute for Smart Cities, Shenzhen University, Shenzhen, 518060, China.
Sci Data. 2024 Sep 7;11(1):980. doi: 10.1038/s41597-024-03776-1.
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.
需要大型数据集来开发人工智能（AI）模型，以助力智能农业，从而减轻农民的日常工作量。本文贡献了首个大型狮头鹅数据集 GooseDetect，该数据集包含 2660 张图像和 98111 个边界框注释。该数据集是在中国广东省汕头市澄海区的一个鹅养殖场使用 6 个摄像机收集的。从 2022 年 7 月 9 日至 10 日采集的视频中采样的图像由 50 名志愿者组成的团队进行了全面注释。与文献中另外 6 个知名动物数据集相比，我们的数据集具有更高的容量和密度，为主流目标检测器提供了具有挑战性的检测基准。在 GooseDetect 上评估了六种最先进的目标检测算法，其中包括一种两阶段基于锚点的检测器、三种一阶段基于锚点的检测器以及两种一阶段无锚点的检测器。结果表明，一阶段基于锚点的检测器 You Only Look Once 版本 5（YOLO v5）在检测精度、模型大小和推理效率方面表现出最佳的整体性能。
