Crack Detection

Project Overview

This project focuses on Crack Detection using a dataset of labeled images to build and evaluate a machine learning model. The dataset includes a total of 32,107 images categorized into cracks and no-cracks. The aim is to automate the detection of cracks, which can have significant applications in structural health monitoring.

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Dataset

The dataset used for this project is available on Google Drive and can be accessed here(zip file also included in the repo).

Key Details:

• Total images: 32,107
• File format: JPG
• Structure:
  • Training set
  • Test set

Data Preparation:

1. Download the .zip file containing the dataset.
2. Extract the contents into a local directory (e.g., /tmp/CrackDetection).
3. The extracted dataset includes structured subdirectories for training and testing.

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Key Features

• Dataset Preparation: Automated downloading and extraction of data.
• Model Development: Implementation of machine learning techniques for crack detection.
• Evaluation Metrics: Accuracy, precision, recall, and F1-score to measure model performance.

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How to Run

1. Clone the repository:

   git clone https://github.com/yourusername/crack-detection.git
   cd crack-detection

2. Install dependencies:

   pip install -r requirements.txt

3. Download the dataset and extract it to the specified location.

4. Run the Jupyter Notebook:

   jupyter notebook finalProject.ipynb

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Future Improvements

• Incorporate additional preprocessing techniques to enhance model accuracy.
• Experiment with advanced architectures, such as CNNs.
• Optimize hyperparameters for better performance.

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Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.

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