Repository for the paper Semantic Segmentation of Underwater Imagery: Dataset and Benchmark (IROS 2020)
• Paper (ArXiv) • Virtual Talk • SUIM Dataset • Experimental data • Checkpoints
SUIM Dataset
- For semantic segmentation of natural underwater images
- 1525 annotated images for training/validation and 110 samples for testing
- BW: Background/waterbody • HD: human divers • PF: Aquatic plants and sea-grass • WR: Wrecks/ruins
- RO: Robots/instruments • RI: Reefs/invertebrates • FV: Fish and vertebrates • SR: Sea-floor/rocks
SUIM-Net Model
- A fully-convolutional encoder-decoder network
- SUIM-Net (RSB): simple and light model; offers reasonable performance at a fast rate
- SUIM-Net (VGG): provides better generalization performance
- Detailed architecture is in models; associated train/test scripts are also provided
- The get_f1_iou.py script is used for performance evaluation
Benchmark Evaluation
- Performance analysis for semantic segmentation and saliency prediction
- SOTA models in comparison: • FCN • UNet • SegNet • PSPNet • DeepLab-v3
- Metrics: • region similarity (F score) and • contour accuracy (mIOU)
- Download experimental data from here and checkpoints data from here
Bibliography Entry
@inproceedings{islam2020suim,
title={{Semantic Segmentation of Underwater Imagery: Dataset and Benchmark}},
author={Islam, Md Jahidul and Edge, Chelsey and Xiao, Yuyang and Luo, Peigen and Mehtaz,
Muntaqim and Morse, Christopher and Enan, Sadman Sakib and Sattar, Junaed},
booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
year={2020},
organization={IEEE/RSJ}
}
Acknowledgements
- https://github.com/qubvel/segmentation_models
- https://github.com/divamgupta/image-segmentation-keras
- https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap
- https://github.com/zhixuhao/unet
- https://github.com/aurora95/Keras-FCN
- https://github.com/MLearing/Keras-Deeplab-v3-plus/
- https://github.com/wenguanwang/ASNet