SCoralDet: Efficient real-time underwater soft coral detection with YOLO & SCoralDet Dataset

Efficient methods for underwater coral detection and high-quality underwater coral detection dataset for machine learning and computer vision research.

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🎥 Detection Video

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📅 Recent Updates

• [2024/12/18]: We have uploaded the core improvement source code of SCoralDet! 😊
• [2024/12/16]: We are maintaining the SCoralDet Dataset on Papers with Code.
• [2024/12/13]: Our paper SCoralDet: Efficient real-time underwater soft coral detection with YOLO has recently been published! [Paper]
• [2024/12/01]: We’ve added specific instructions on how to use the dataset for your research!
• [2024/11/20]: We've opened up our dataset! [SCoralDet-Dataset]

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📝 Overview

The SCoralDet Dataset is a high-quality underwater coral dataset, containing 646 annotated images collected from the Coral Germplasm Conservation and Breeding Center at Hainan Tropical Ocean University in Sanya, China. This dataset is specifically designed for coral detection and recognition tasks, making it ideal for researchers and practitioners working on underwater image analysis, marine biology, and computer vision applications.

🌊 Coral Species:

The dataset includes images from the following six coral species:

• Euphylliaancora
• Favosites
• Platygyra
• Sarcophyton
• Sinularia
• Wavinghand

📂 Dataset Details:

• Total images: 646
• Format: PNG/JPEG
• Annotations: Bounding boxes in COCO format
• Resolution: Variable (high-resolution images)

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📥 Download

You can download the dataset from Google Drive.

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🐚 Dataset Preview

Here are a few samples from the dataset:

Euphylliaancora	Platygyra	Sarcophyton

Wavinghand	Favosites	Sinularia

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🚀 Follow-Up Work: MambaCoral-DiffDet

Building upon the SCoralDet dataset, we developed MambaCoral-DiffDet as a refined and advanced framework for soft coral detection under complex underwater conditions. This work utilizes diffusion models to address data imbalance and improves detection accuracy using novel attention mechanisms and architecture modifications.

For more details, please refer to the MambaCoral-DiffDet, where you can see how SCoralDet has been leveraged and extended in this project.

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🎯 Applications

This dataset can be used for:

• Coral species classification
• Object detection tasks (e.g., YOLO, Faster R-CNN)
• Transfer learning on underwater datasets

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🛠️ How to Use

1. Download the Dataset

To get started with the SCoralDet Dataset, you can download it directly from Google Drive.

2. Preparing the Dataset

Once you have the dataset, unzip the downloaded files. The dataset is organized into the following structure:

/your_dataset_path
├── annotations
└── image
    ├── Euphflfiaancora
    ├── Favosites
    ├── Platygyra
    ├── Sarcophyton
    ├── Sinularia
    └── WavingHand

• annotations/: Contains annotation files in COCO format for each image in the image/ directory.
• image/: Contains images for each of the six coral species, organized in separate subdirectories.

3. Processing the Dataset

You can use the Dataset_process.ipynb file to process the dataset and get the images in PNG/JPEG format with COCO annotations. You can split the dataset for training and validation.

4. Converting COCO to YOLO Format

If you'd like to use the dataset in YOLO format, use the coco2yolo.py script:

python coco2yolo.py --json_path your_coral_instances.json --save_path your_yolo_label_path

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🔍 Citation

If you find our work useful, please consider citing us or giving us a star ⭐.

@ARTICLE{lu2024scoraldet,  
         author={Lu, Zhaoxuan and Liao, Lyuchao and Xie, Xingang and Yuan, Hui},  
         title={SCoralDet: Efficient real-time underwater soft coral detection with YOLO},  
         journal={Ecological Informatics},  
         year={2024},  
         artnum={102937},  
         issn={1574-9541},  
         doi={10.1016/j.ecoinf.2024.102937},  
}  

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✨ Contributions

We welcome contributions to improve this dataset! Feel free to:

• Report issues or bugs.
• Submit pull requests for any enhancements.