🚢 Underwater Object Detection Improvement

Improving object detection models for underwater environments using YOLOv8 and advanced attention mechanisms

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📋 Project Overview

This project focuses on improving underwater object detection models using a large corpus of underwater datasets. By training state-of-the-art models such as YOLOv8 and YOLOv7 and integrating advanced attention mechanisms, we achieved a 3-5% improvement in accuracy. The implementation of the Residual Convolutional Block Attention Module (RESCBAM) into the YOLOv8 backbone contributed to this performance boost, making YOLOv8 a new state-of-the-art model for underwater object detection.

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🎯 Key Achievements

• 📊 Performance Improvement: Achieved a 3-5% accuracy improvement over previous models (YOLOv7).
• 🔍 Advanced Attention Mechanisms: Researched and integrated RESCBAM for better feature extraction and model optimization.
• 🧠 Deep Learning Models: Trained YOLOv8/YOLOv7 on a large-scale underwater dataset, enhancing detection capabilities in complex underwater environments.

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🚀 Methodology

1. Dataset: Utilized a large corpus of underwater images containing various marine objects and species.
2. Model Training: Trained both YOLOv8 and YOLOv7 models, with modifications to the architecture and attention mechanisms.
3. Attention Mechanism: Implemented the Residual Convolutional Block Attention Module (RESCBAM) into the YOLOv8 backbone, enhancing the model’s ability to focus on key features in the images.
4. Evaluation: Compared the performance of YOLOv8 and YOLOv7, showing that YOLOv8 with RESCBAM outperformed YOLOv7 by 3-5% in accuracy.

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📊 Results

• Accuracy: 3-5% improvement in object detection accuracy over YOLOv7.
• State-of-the-Art: Established YOLOv8 as the new state-of-the-art for underwater object detection with RESCBAM integration.

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🛠️ Technologies Used

• YOLOv8/YOLOv7: State-of-the-art object detection models.
• RESCBAM: Residual Convolutional Block Attention Module for feature enhancement.
• Python: Programming language for model training and evaluation.
• PyTorch: Deep learning framework used for model development and training.
• OpenCV: For image preprocessing and augmentation.

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💡 Future Improvements

• Further fine-tuning of the attention mechanisms for more diverse underwater environments.
• Exploring additional datasets for broader generalization across different underwater conditions.

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👨‍💻 Contributing

Feel free to contribute by submitting issues or pull requests. All contributions are welcome!

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📄 License

This project is licensed under the MIT License. See the LICENSE file for more details.

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🔗 References

• YOLOv8 Documentation
• RESCBAM Paper