NDD20: A large-scale few-shot dolphin dataset for coarse and fine-grained categorisation

By Cameron Trotter, Georgia Atkinson, Matt Sharpe, Kirsten Richardson, Stephen Mcgough, Nick Wright, Ben Burville, Per Berggren. 2020

33 citations recorded by the source corpus.

We introduce the Northumberland Dolphin Dataset 2020 (NDD20), a challenging image dataset annotated for both coarse and fine-grained instance segmentation and categorisation. This dataset, the first release of the NDD, was created in response to the rapid expansion of computer vision into conservation research and the production of field-deployable systems suited to extreme environmental conditions -- an area with few open source datasets. NDD20 contains a large collection of above and below water images of two different dolphin species for traditional coarse and fine-grained segmentation. All data contained in NDD20 was obtained via manual collection in the North Sea around the Northumberland coastline, UK. We present experimentation using standard deep learning network architecture trained using NDD20 and report baselines results.

Subfields: Computer Vision and Pattern Recognition, Ecology, Oceanography, Water Science and Technology, Evaluation & Benchmarks

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In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Ming-Hsuan Yang
3. Dacheng Tao
4. Jitendra Malik
5. Trevor Darrell
6. Xiaogang Wang
7. Chunhua Shen
8. Andrew Zisserman

In Evaluation & Benchmarks

1. Ross B. Girshick
2. Fisher Yu
3. Alexander Kirillov
4. Christoph Feichtenhofer
5. Piotr Doll'ar
6. Hanzi Mao
7. Tete Xiao
8. Alexander C. Berg

Citations in the corpus

• Segment Anything (2023, background): “ADE20K[117] BBBC038v1[12] Cityscapes[25] DOORS[80] DRAM[24] EgoHOS[113] GTEA[34,63] Hypersim[86] IBD[17] iShape[111] LVIS[44] NDD20[100] NDISPark[22,23] OVIS[81] PPDLS[74] Plittersdorf[46] STREETS[91] TimberSeg[38] TrashCan[52] VISOR[28,27] WoodScape[112] PIDRay[104] ZeroWaste-f[6] Figure 8: Samples”
• Segment Anything in High Quality (2023, methodology): “In Section 6.4, we show extensive visual results comparison between our HQ-SAM and SAM on COCO [31], DIS-test [34], HR-SOD [50], NDD20 [40], DAVIS [33], and YTVIS [46].”
• SAM 2: Segment Anything in Images and Videos (2024, background): “…2022), DRAM (Cohen et al., 2022), EgoHOS (Zhang et al., 2022), GTEA (Fathi et al., 2011; Li et al., 2015), iShape (Yang et al., 2021a), NDD20 (Trotter et al., 2020), NDISPark (Ciampi et al., 2021, 2022), OVIS (Qi et al., 2022), PPDLS (Minervini et al., 2016), Plittersdorf (Haucke et al.,…”