Automate vehicle damage inspection by harnessing CNNs and transfer‑learning to classify images
The Car Damage Detection System leverages convolutional neural networks and transfer learning to:
- Automatically identify vehicle damage from front and rear images.
- Classify damage into six categories: Front Normal, Front Breakage, Front Crushed, Rear Normal, Rear Breakage, Rear Crushed.
- Accelerate and standardize the vehicle return inspection process.
- Baseline CNN Module: Custom CNN architecture achieving a strong starting accuracy.
- Regularization & Augmentation: Batch normalization, dropout, and extensive image augmentations for robustness.
- Transfer Learning: Leveraging pre-trained EfficientNet B0 and ResNet50 backbones to boost performance on limited data.
- Hyperparameter Tuning: Optuna-driven search for optimal learning rate and dropout settings.
- Model Evaluation: Detailed classification report and confusion matrix for per-class performance analysis.
- Interactive Streamlit App: User-friendly interface to upload or drag-and-drop an image and view predictions instantly.
https://car-damage-detection-new.streamlit.app/
app/
│
├── model/
│ ├── saved_model.pth # Trained DL model for damage classification
│
├── app.py # Core Streamlit application
├── model_helper.py # Using model to classify the image
├── app_screenshot.jpg # Local demo image for README
├── .gitignore # Git ignore rules
├── requirements.txt # Project dependencies
└── README.md # Project overview and instructions
-
Clone the Repository
git clone https://github.com/MindMatrixPro/Car-Damage-Detection.git cd Car-Damage-Detection -
Install Dependencies
pip install -r requirements.txt
-
Run the Streamlit App
streamlit run app.py
To Contribute, please submit issues or pull requests for enhancements or fixes.
Licensed under the Apache 2.0 License.