Semantic Segmentation of Underwater Imagery: Dataset and Benchmark
Md Jahidul Islam, Chelsey Edge, Yuyang Xiao, Peigen Luo, Muntaqim Mehtaz, Christopher Morse, Sadman Sakib Enan, Junaed Sattar
Abstract
In this paper, we present the first large-scale dataset for semantic Segmentation of Underwater IMagery (SUIM). It contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants. We also present a comprehensive benchmark evaluation of several state-of-the-art semantic segmentation approaches based on standard performance metrics. Additionally, we present SUIM-Net, a fully-convolutional deep residual model that balances the trade-off between performance and computational efficiency. It offers competitive performance while ensuring fast end-to-end inference, which is essential for its use in the autonomy pipeline by visually-guided underwater robots. In particular, we demonstrate its usability benefits for visual servoing, saliency prediction, and detailed scene understanding. With a variety of use cases, the proposed model and benchmark dataset open up promising opportunities for future research in underwater robot vision.
BibTeX
@inproceedings{iros2020_semanticsegmenta,
title = {Semantic Segmentation of Underwater Imagery: Dataset and Benchmark},
author = {Md Jahidul Islam and Chelsey Edge and Yuyang Xiao and Peigen Luo and Muntaqim Mehtaz and Christopher Morse and Sadman Sakib Enan and Junaed Sattar},
booktitle = {IROS 2020},
year = {2020}
}