Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments

Context This dataset is part of the paper "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments" presented at Oceans 2022, Hampton Roads, DOI: 10.1109/OCEANS47191.2022.9977024 This dataset consists of paired camera and multi-beam sonar images of technical divers performing different underwater tasks in two locations: an indoor test basin and a lake. The general goal is to assist emergency operators that monitor the safety of divers operating in bad visibility conditions. This data was used to train image-to-image translation models in order to generate realistic optical-like images given only sonar images as input or a combination of a sonar image and a dark or turbid optical image. Content This repository contains three .zip folders each containing data collected in a different lab or field trial. 'basin-dataset-1.zip' and 'basin-dataset-2.zip' contain data that were collected in an indoor testing facility at DFKI - Robotics Innovation Center, Bremen, Germany. 'lake-dataset-1.zip' and 'lake-dataset-2.zip' contains data collected at lake Kreidesee, Hemmoor, Germany. Each .zip file contains two subfolders labelled as 'camera' and 'sonar', each containing the images in png format. Data files under these subfolders with matching names composes a pair of time-synchronized images. For example, 'camera/0001.png' corresponds to 'sonar/0001.png'. The acquisition timestamp represented in seconds since epoch for every data file is recorded in 'sample.csv' include in each .zip file. For more details and meta-information on the collected data please refer to "data_description.json" included in this repository. Additional tools for handling and preparing the data can be found under https://github.com/DeeperSense/oceans_2022 Acknowledgements The data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958. The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their initiative within the framework of the NFDI4Ing consortium (German Research Foundation (DFG) - project number 442146713).## 背景（Context） 本数据集隶属于发表于2022年Oceans大会（汉普顿锚地）的论文"低能见度环境下用于潜水员监测的声呐到RGB图像转换技术"，DOI为10.1109/OCEANS47191.2022.9977024。 本数据集包含配对的相机图像与多波束声呐（multi-beam sonar）图像，采集自两类场景：技术潜水员在室内试验水池及湖泊中执行不同水下任务的过程。本数据集的核心应用目标为辅助应急操作人员，对低能见度环境下作业的潜水员开展安全监测。 该数据集被用于训练图像到图像转换（image-to-image translation）模型，以实现仅以声呐图像作为输入，或结合声呐图像与昏暗/浑浊光学图像，生成逼真的类光学图像。 ## 内容（Content） 本数据集仓库包含三个压缩包文件夹，分别对应不同实验室或野外试验采集的数据。 `basin-dataset-1.zip`与`basin-dataset-2.zip`包含在德国不来梅德国人工智能研究中心（DFKI）机器人创新中心室内试验场地采集的数据。 `lake-dataset-1.zip`与`lake-dataset-2.zip`则包含在德国亨莫尔市克莱德泽湖（Lake Kreidesee）采集的数据。 每个压缩包均包含两个子文件夹，分别命名为`camera`与`sonar`，内部均存储PNG（Portable Network Graphics）格式的图像文件。两个子文件夹中文件名一致的数据文件构成一组时间同步的图像对。例如，`camera/0001.png`与`sonar/0001.png`即为一组同步图像。每个压缩包内的`sample.csv`文件记录了所有数据文件的采集时间戳（以纪元秒为单位）。 若需了解采集数据的更多细节与元信息，请查阅本仓库内的`data_description.json`文件。 用于数据处理与预处理的额外工具可访问以下链接获取：https://github.com/DeeperSense/oceans_2022。 ## 致谢（Acknowledgements） 本仓库中的数据集由德国人工智能研究中心（DFKI）、德国联邦技术救援局（THW）以及Kraken Robotics GmbH联合采集完成。本研究隶属于DeeperSense项目，该项目获欧盟委员会H2020-ICT-2020-2 ICT-47-2020计划资助，项目编号为101016958。 作者感谢联邦政府、各州政府首脑以及联合科学会议（GWK）在NFDI4Ing联盟框架内提供的支持，该联盟由德国研究基金会（DFG）资助，项目编号为442146713。