TrashCan: 海洋ごみの視覚的検出に向けた意味分割データセット
TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris
水中ごみの画像をバウンディングボックスとセグメンテーションラベル付きで収集した大規模データセットTrashCanを構築し、Mask R-CNNとFaster R-CNNによる初期ベースライン結果を示した論文。
著者: Jungseok Hong, Michael Fulton, Junaed Sattar
分類: cs.CV, cs.RO
原文アブストラクト
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.
