AMSP-UOD | AAAI-24

The repository is an open source repository for AMSP-UOD, which aims to provide some new ideas for underwater object detection, and the paper has been accepted by AAAI-24.

Quick Start

This code repository includes the base run code for AMSP-UOD, see folder ./weights for the weights file, and see folder ./result for the PR curve graph. There is some of the urpc test data in folder ./urpc, and you can get the recognition results using ./det.sh

1. Deploy Conda environment

conda create -n AMSP_UOD python==3.10

2. Install package dependencies

pip install -r requirements.txt

3. Train Model (Optional, requires Datasets and Cuda)

Our default code uses NMS-Similar algorithm from the paper, and you should either turn off val validation or post-process that use the traditional NMS algorithm before training. This is done by switching the file ./utils/general.py from lines 950 to 953.

conda activate AMSP_UOD
./train.sh 0

4. Test Model (Optional, requires Datasets and Cuda)

Note that when testing model performance, change the val option in urpc.yaml from self-divided data to urpc's B-list data.

conda activate AMSP_UOD
./val.sh

5. Dectet

conda activate AMSP_UOD
./det.sh

Showcase

URPC

RUOD

For more details check out ./result folder, we give the experimental result plots for some of the ablation experiments.

Cite

You can cite our work in the following format:

arXiv

@article{zhou2023amsp,
  title={AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection},
  author={Zhou, Jingchun and He, Zongxin and Lam, Kin-Man and Wang, Yudong and Zhang, Weishi and Guo, ChunLe and Li, Chongyi},
  journal={arXiv preprint arXiv:2308.11918},
  year={2023}
}

AAAI-24

@inproceedings{AMSP-UOD,
  title={AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection},
  author={Zhou, Jingchun and He, Zongxin and Lam, Kin-Man and Wang, Yudong and Zhang, Weishi and Guo, ChunLe and Li, Chongyi},
  booktitle={Proceedings of the AAAI conference on artificial intelligence},
  volume={},
  number={},
  pages={},
  year={2024}
}