TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris

TL;DR

TrashCan is a comprehensive underwater marine debris dataset with annotations for developing and benchmarking robust detection methods suitable for robotic applications.

Contribution

The paper introduces the TrashCan dataset with semantic segmentation and bounding box annotations, enabling future research in marine debris detection.

Findings

Initial Mask R-CNN results for instance segmentation.

Initial Faster R-CNN results for object detection.

Dataset supports development of onboard marine debris detection systems.

Abstract

This paper presents TrashCan, a large dataset comprised of images of underwater trash collected from a variety of sources, annotated both using bounding boxes and segmentation labels, for development of robust detectors of marine debris. The dataset has two versions, TrashCan-Material and TrashCan-Instance, corresponding to different object class configurations. The eventual goal is to develop efficient and accurate trash detection methods suitable for onboard robot deployment. Along with information about the construction and sourcing of the TrashCan dataset, we present initial results of instance segmentation from Mask R-CNN and object detection from Faster R-CNN. These do not represent the best possible detection results but provides an initial baseline for future work in instance segmentation and object detection on the TrashCan dataset.