OzFish Dataset - Machine learning dataset for Baited Remote Underwater Video Stations

This dataset has been developed as part of the Australian Research Data Commons Data Discoveries program (https://ardc.edu.au/project/machine-learning-dataset-creation-for-australian-fish-species-from-baited-remote-underwater-videos-bruv/), with the aim to futher advance research into machine learning for the automated detection of fish from video. The dataset was generated from over 3000 videos which were historically analysed with the Event Measure software package and sourced from the Australian Institute of Marine Science (AIMS), University of Western Australia (UWA) and Curtin University of Technology.

The dataset is comprised of the following:
- ~80k labelled crops of fish extracted from the videos, from over 500 species, 200 genera and 70 families
- ~45k bounding box annotations (suitable for YOLO,RetinaNet) of fish/no fish across 1800 frames

本数据集作为澳大利亚研究数据共享平台（Australian Research Data Commons，ARDC）“数据发现”项目的一部分开发完成，项目链接为https://ardc.edu.au/project/machine-learning-dataset-creation-for-australian-fish-species-from-baited-remote-underwater-videos-bruv/，旨在进一步推进基于机器学习的水下视频鱼类自动检测相关研究。
本数据集源自超过3000段视频，这些视频既往已通过Event Measure软件包完成分析，数据来源于澳大利亚海洋科学研究所（Australian Institute of Marine Science，AIMS）、西澳大利亚大学（University of Western Australia，UWA）以及科廷科技大学（Curtin University of Technology）。

本数据集包含以下内容：
- 约8万张从视频中提取的鱼类标注裁剪样本，涵盖500余个物种、200余个属以及70余个科
- 约4.5万份边界框标注（适用于YOLO、RetinaNet模型），覆盖1800帧图像，标注类别包含鱼类与非鱼类目标

搜集汇总

背景与挑战

背景概述

该数据集名为OzFish Dataset，是一个用于机器学习的数据集，专门针对诱饵远程水下视频站。它旨在通过来自3000多个历史视频的数据，推动鱼类自动检测的机器学习研究。数据集是澳大利亚研究数据共享计划的一部分，原始视频曾使用Event Measure软件进行分析。