TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris
By Jungseok Hong, Michael Fulton, Junaed Sattar. 2020
136 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Pollution, Water Science and Technology, Evaluation & Benchmarks
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Jitendra Malik
- Dacheng Tao
- Xiaogang Wang
- Trevor Darrell
- Alan L. Yuille
- Ming-Hsuan Yang
- Bernt Schiele
In Evaluation & Benchmarks
- Ross B. Girshick
- Alexander Kirillov
- Yoshua Bengio
- Piotr Doll'ar
- Hanzi Mao
- Tete Xiao
- Alexander C. Berg
- Jie Fu
Citations in the corpus
- Segment Anything (2023, methodology): “-20 0 +20 +40 IoU delta at 1 center point GTEA [34, 63] TrashCan [52] DRAM [24] PIDRay [104] Cityscapes [25] WoodScape [112] IBD [17] EgoHOS [113] Plittersdorf [46] VISOR [28, 27] NDISPark [22, 23] Hypersim [86] OVIS [81] ADE20K [117] iShape [111] ZeroWaste-f [6] STREETS [91] LVIS [44] NDD20 [100] T”
- FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters (2021, background): “Based on these datasets, works have been done for vision-based waste detection [14, 10, 18].”
- SAM 2: Segment Anything in Images and Videos (2024, unknown): “…2022), PPDLS (Minervini et al., 2016), Plittersdorf (Haucke et al., 2022), STREETS (Snyder & Do, 2019), TimberSeg (Fortin et al., 2022), TrashCan (Hong et al., 2020), VISOR (Darkhalil et al., 2022; Damen et al., 2022), WoodScape (Yogamani et al., 2019), PIDRay (Wang et al., 2021a), ZeroWaste-f…”