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

本数据集系澳大利亚研究数据共享平台（Australian Research Data Commons，ARDC）数据发现项目（https://ardc.edu.au/project/machine-learning-dataset-creation-for-australian-fish-species-from-baited-remote-underwater-videos-bruv/）的研究产出，旨在推进面向视频中鱼类自动检测任务的机器学习研究。
该数据集源自超过3000段水下视频，这些视频此前已通过Event Measure软件包完成标注分析，数据来源涵盖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）。
本数据集包含如下内容：
1. 从上述视频中提取的约8万条鱼类标注裁剪样本，覆盖超过500个鱼类物种、200个属以及70个科；
2. 约4.5万张边界框标注（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两类目标。

搜集汇总

背景与挑战

背景概述

OzFish Dataset是一个专为机器学习研究设计的鱼类检测数据集，包含约80,000个标记的鱼类裁剪图像和45,000个边界框注释，涵盖500多种鱼类。数据来源于澳大利亚海洋科学研究所等机构的3,000多个历史视频，旨在推动从水下视频中自动检测鱼类的研究。