Fulton, Michael SHong, JungseokSattar, Junaed2020-07-212020-07-212020-07-21https://hdl.handle.net/11299/214366This dataset is available for download as a .zip file named Trash_ICRA19.zip. Within the compressed folder are both the dataset and configurations for testing the datset with deep learning algorithms. There is a README in the top directory, and in most lower directories, explaining the files and directories in its directory.This 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.Free for academic teaching/research use, must obtain JAMSTEC permission for commercial use. See LICENSE.txt for further informationmarinegarbagetrashlitterunderwaterdebrisDatasethttps://doi.org/10.13020/x0qn-y082