A Deep-Learning Approach for Visual Detection of an AUV Docking Station - Dataset

This dataset was used to train the models from the paper "A Deep-Learning Approach for Visual Detection of an AUV Docking Station" published by Ahmad et al. at Oceans 2024 Conference in Halifax.

# Dataset

The dataset contains:

1. images from Abisko Lake in Sweden[1]

2. images recorded in the Maritime Hall basin at DFKI

# File contents

Each of these datasets are put into seperate directories. The images were annotated using CVAT[2].

The dataset has been exported into the following formats:

1. YOLO

2. PascalVOC

3. COCO

The exported datasets does not contain raw images, rather they are places into a seperate zip folder.

# References

[1]: https://zenodo.org/record/7035132#.ZDfKE5FBzJU

[2]: https://www.cvat.ai/

本数据集用于训练Ahmad等人于2024年哈利法克斯海洋学大会（Oceans 2024 Conference）发表的论文《面向自主水下航行器（Autonomous Underwater Vehicle, AUV）对接站视觉检测的深度学习方法》中的模型。

# 数据集
本数据集包含：
1. 瑞典阿比斯库湖（Abisko Lake）的图像[1]
2. 德国人工智能研究中心（DFKI）海事大厅水池的录制图像

# 文件内容
所有数据集均存放于独立目录中。图像均通过CVAT（Computer Vision Annotation Tool）[2]完成标注。
本数据集已导出为以下格式：
1. YOLO
2. PascalVOC
3. COCO
导出的数据集未包含原始图像，原始图像被单独打包至一个ZIP压缩文件夹中。

# 参考文献
[1]: https://zenodo.org/record/7035132#.ZDfKE5FBzJU
[2]: https://www.cvat.ai/