水下垃圾检测数据集

AbstractThis data was sourced from the J-EDI dataset of marine debris. The videos that comprise that dataset vary greatly in quality, depth, objects in scenes, and the cameras used. They contain images of many different types of marine debris, captured from real-world environments, providing a variety of objects in different states of decay, occlusion, and overgrowth. Additionally, the clarity of the water and quality of the light vary significantly from video to video. These videos were processed to extract 5,700 images, which comprise this dataset, all labeled with bounding boxes on instances of trash, biological objects such as plants and animals, and ROVs. The eventual goal is to develop efficient and accurate trash detection methods suitable for onboard robot deployment. It is our hope that the release of this dataset will facilitate further research on this challenging problem, bringing the marine robotics community closer to a solution for the urgent problem of autonomous trash detection and removal.Referenced byM. Fulton, J. Hong, M. J. Islam and J. Sattar, "Robotic Detection of Marine Litter Using Deep Visual Detection Models," 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 5752-5758https://doi.org/10.1109/ICRA.2019.8793975Suggested CitationFulton, Michael S; Hong, Jungseok; Sattar, Junaed. (2020).Trash-ICRA19: A Bounding Box Labeled Dataset of Underwater Trash.Retrieved from the Data Repository for the University of Minnesota, https://doi.org/10.13020/x0qn-y082.

摘要
本数据集源自海洋垃圾J-EDI数据集（J-EDI dataset of marine debris）。该数据集包含的视频在画质、拍摄深度、场景内物体以及所用相机方面差异显著。这些视频涵盖了从真实环境中采集的多种不同类型海洋垃圾图像，包含不同腐烂程度、遮挡情况和附生状态的各类物体。此外，不同视频间的水体透明度与光照质量也存在显著差异。

研究人员对这些视频进行处理，从中提取出5700张图像，构成本数据集，所有图像均针对垃圾实例、植物、动物等生物物体以及遥控无人潜水器（Remotely Operated Vehicles，ROVs）标注了边界框。

本数据集的最终目标是开发适用于机器人机载部署的高效且精准的垃圾检测方法。我们期望该数据集的发布能够推动针对这一极具挑战性的课题的进一步研究，助力海洋机器人领域逐步解决自主垃圾检测与清理这一紧迫问题。

被以下文献引用：
M. Fulton、J. Hong、M. J. Islam 与 J. Sattar，《基于深度视觉检测模型的海洋垃圾机器人检测》，2019年国际机器人与自动化会议（International Conference on Robotics and Automation，ICRA），加拿大魁北克省蒙特利尔，2019年，第5752-5758页。DOI: 10.1109/ICRA.2019.8793975

推荐引用格式：
Fulton, Michael S; Hong, Jungseok; Sattar, Junaed. (2020). Trash-ICRA19: 一个带边界框标注的水下垃圾数据集. 检索自明尼苏达大学数据仓储，https://doi.org/10.13020/x0qn-y082.

搜集汇总

背景与挑战

背景概述

该数据集包含5,700张从真实海洋环境中捕获的图像，涵盖多种状态的垃圾和生物物体，已标注边界框，旨在支持机器人部署的垃圾检测方法研究。