Tasmanian Orange Roughy Stereo Image Machine Learning Dataset

The Tasmanian Orange Roughy Stereo Image Machine Learning Dataset is a collection of annotated stereo image pairs collected by a net-attached Acoustic and Optical System (AOS) during orange roughy (Hoplostethus atlanticus) biomass surveys off the northeast coast of Tasmania, Australia in July 2019. The dataset consists of expertly annotated imagery from six AOS deployments (OP12, OP16, OP20, OP23, OP24, and OP32), representing a variety of conditions including different fish densities, benthic substrates, and altitudes above the seafloor. Each image was manually annotated with bounding boxes identifying orange roughy and other marine species. For all annotated images, paired stereo images from the opposite camera have been included where available to enable stereo vision analysis. This dataset was specifically developed to investigate the effectiveness of machine learning-based object detection techniques for automating fish detection under variable real-world conditions, providing valuable resources for advancing automated image processing in fisheries science.

塔斯马尼亚橘棘鲷立体图像机器学习数据集是一套经标注的立体图像对集合，由搭载于网具的声光系统（Acoustic and Optical System，AOS）于2019年7月在澳大利亚塔斯马尼亚东北海域开展的橘棘鲷（Hoplostethus atlanticus）生物量调查期间采集。本数据集包含6次声光系统布放（OP12、OP16、OP20、OP23、OP24及OP32）所获取的经专业人员标注的影像数据，涵盖不同鱼类密度、底栖基底类型以及离海底高度等多种实地环境条件。每幅影像均通过人工标注边界框，以标识橘棘鲷及其他海洋物种。对于所有标注影像，若有可用的另一侧相机拍摄的配对立体影像，均一并纳入以支持立体视觉分析。本数据集专为探究基于机器学习的目标检测技术在多变实地环境下实现鱼类自动化检测的效能而构建，可为渔业科学领域自动化图像处理技术的发展提供宝贵的研究资源。

搜集汇总

背景与挑战

背景概述

该数据集是一个专门为机器学习对象检测设计的塔斯马尼亚橘棘鲷立体图像集合，采集于2019年7月澳大利亚塔斯马尼亚东北海岸的生物量调查。它包括1901个文件，其中1051张图像经过专家手动标注，涵盖橘棘鲷和其他海洋物种的边界框，并包含849张配对立体图像以支持立体视觉分析。数据集设计用于评估在不同鱼类密度、底质类型和高度等真实世界条件下自动化鱼类检测技术的有效性，为渔业科学中的图像处理提供资源。