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💣 Bomb Detection Model using AI & Computer Vision

📌 Project Overview

The Bomb Detection Model is an AI-powered system designed to identify potential explosive devices in images using computer vision and machine learning techniques.
The goal of this project is to assist in early threat detection, public safety, and surveillance applications by reducing human error and response time.

This system analyzes uploaded images and classifies them as bomb/explosive or non-threatening objects based on learned visual patterns.

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🎯 Objectives

• Detect explosive devices from images with high accuracy
• Minimize false positives from everyday objects
• Provide real-time or near real-time predictions
• Build a scalable and deployable safety-focused AI system

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🧠 How It Works

1. User uploads an image through the interface
2. Image is preprocessed (resizing, normalization, noise reduction)
3. The trained ML/CNN model extracts visual features
4. Model predicts whether the object is:
   • Explosive Device
   • Non-Explosive / Safe Object
5. Result is displayed with confidence score

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🛠️ Tech Stack

• Programming Language: Python
• Libraries & Frameworks:
  • TensorFlow / PyTorch
  • OpenCV
  • NumPy
  • Matplotlib
• Model Type: Convolutional Neural Network (CNN)
• Frontend (optional): HTML, CSS, JavaScript
• Backend (optional): Flask / FastAPI

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📂 Project Structure

bomb-detection-model/ │ ├── dataset/ │ ├── explosive/ │ └── non_explosive/ │ ├── model/ │ ├── trained_model.h5 │ └── model_training.py │ ├── app/ │ ├── app.py │ └── templates/ │ ├── utils/ │ └── preprocessing.py │ ├── requirements.txt ├── README.md └── LICENSE

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2. Create and activate virtual environment

```bash
python -m venv venv
source venv/bin/activate   # Windows: venv\Scripts\activate
```

3. Install dependencies

```bash
pip install -r requirements.txt
```

4. Run the application

```bash
python app.py
```

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 📊 Dataset

* Contains labeled images of:

  * Explosive devices
  * Non-explosive objects
* Images are preprocessed and augmented for better generalization
* Dataset can be expanded for higher accuracy

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## ✅ Features

* Image-based bomb detection
* Fast and automated threat analysis
* Scalable architecture
* Easy integration with security systems
* User-friendly interface

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## ⚠️ Limitations

* Performance depends on dataset quality
* May struggle with low-resolution or obscured images
* Not intended to replace professional security checks

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## 🔮 Future Enhancements

* Live CCTV / video stream detection
* Multi-class classification (guns, bombs, knives)
* Edge deployment (IoT / Raspberry Pi)
* Improved accuracy with larger datasets
* Real-time alert system integration

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## 🧪 Use Cases

* Airports & railway stations
* Public surveillance systems
* Smart city infrastructure
* Defense and military research
* Hackathons & academic projects

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📜 Disclaimer

This project is developed **strictly for educational and research purposes**.
It must not be used for illegal activities or real-world deployment without proper authorization and testing.



👤 Author

**Ayush**
AI/ML Developer | Hackathon Finalist | Product Builder



## ⭐ Acknowledgements

* Open-source datasets
* TensorFlow / PyTorch community
* Computer Vision research papers

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## 📌 License

This project is licensed under the **MIT License**.