Efficient Marine Organism Detector (EMOD) for Marine Video Surveillance
We release the code of Efficient Marine Organism Detector (EMOD) in our papers:
- Detecting Marine Organisms via Joint Attention-Relation Learning for Marine Video Surveillance. (IEEE Journal of Oceanic Engineering, 2022, DOI: 10.1109/JOE.2022.3162864 (DOI currently unavailable))
- Detecting Organisms for Marine Video Surveillance. (Global OCEANS 2020, DOI: 10.1109/IEEECONF38699.2020.9389458)
@inproceedings{shi2020detecting,
title={Detecting Organisms for Marine Video Surveillance},
author={Shi, Zhensheng and Guan, Cheng and Cao, Liangjie and Li, Qianqian and Liang, Ju and Guo, Zonghui and Gu, Zhaorui and Zheng, Haiyong and Zheng, Bing},
booktitle={Global OCEANS 2020: Singapore--US Gulf Coast},
pages={1--7},
year={2020},
organization={IEEE}
}
Introduction
We design an Efficient Marine Organism Detector (EMOD) for high-resolution marine video surveillance to detect organisms and surveil marine environments in a real-time and fast fashion. We also propose a novel Attention-Relation (AR) module to explore joint Attention-Relation in CNNs for marine organism detection. This code is based on the mmdetection codebase (v2.13.0).
Requirements
- Linux or macOS (Windows is in experimental support)
- Python 3.6+
- PyTorch 1.3+
- CUDA 9.2+ (If you build PyTorch from source, CUDA 9.0 is also compatible)
- GCC 5+
- MMCV
Datasets
HabCam, MOUSS and MBARI.
- Contact dataset provider, and download the datasets and annotations: CVPR 2018 Workshop or CVPR 2019 Workshop.
- Put all images and annotation files to $EMOD/data folder.
Running
-
To train a FR-R50 detector on HABCAM dataset, you can run the script:
CUDA_VISIBLE_DEVICES=0,1 PORT=29500 tools/dist_train.sh configs-emod/FR/FR_R50_FPN_HABCAM_SP1.py 2
You can also set the variables (CONFIG_FILE, GPU_NUM) in scripts/run_habcam.sh, and then run the script:
bash scripts/run_habcam.sh
Models
We will provide the models and results later.
Acknowledgement
We really appreciate the contributors of following codebases.