CoralVOS: Dataset and Benchmark for Coral Video Segmentation

• URL: http://arxiv.org/abs/2310.01946v1
• Date: Tue, 3 Oct 2023 10:45:37 GMT
• Title: CoralVOS: Dataset and Benchmark for Coral Video Segmentation
• Authors: Zheng Ziqiang, Xie Yaofeng, Liang Haixin, Yu Zhibin, Sai-Kit Yeung
• Abstract summary: We propose a large-scale coral video segmentation dataset: textbfCoralVOS as demonstrated in Fig. 1. We perform experiments on our CoralVOS dataset, including 6 recent state-of-the-art video object segmentation (VOS) algorithms. The results show that there is still great potential for further promoting the segmentation accuracy.
• Score: 12.434773034255455
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Coral reefs formulate the most valuable and productive marine ecosystems, providing habitat for many marine species. Coral reef surveying and analysis are currently confined to coral experts who invest substantial effort in generating comprehensive and dependable reports (\emph{e.g.}, coral coverage, population, spatial distribution, \textit{etc}), from the collected survey data. However, performing dense coral analysis based on manual efforts is significantly time-consuming, the existing coral analysis algorithms compromise and opt for performing down-sampling and only conducting sparse point-based coral analysis within selected frames. However, such down-sampling will \textbf{inevitable} introduce the estimation bias or even lead to wrong results. To address this issue, we propose to perform \textbf{dense coral video segmentation}, with no down-sampling involved. Through video object segmentation, we could generate more \textit{reliable} and \textit{in-depth} coral analysis than the existing coral reef analysis algorithms. To boost such dense coral analysis, we propose a large-scale coral video segmentation dataset: \textbf{CoralVOS} as demonstrated in Fig. 1. To the best of our knowledge, our CoralVOS is the first dataset and benchmark supporting dense coral video segmentation. We perform experiments on our CoralVOS dataset, including 6 recent state-of-the-art video object segmentation (VOS) algorithms. We fine-tuned these VOS algorithms on our CoralVOS dataset and achieved observable performance improvement. The results show that there is still great potential for further promoting the segmentation accuracy. The dataset and trained models will be released with the acceptance of this work to foster the coral reef research community.

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