/AI-ML-Project

This repository is dedicated to building a computer vision model, primarily focused on detecting potholes. The model is trained on a variety of datasets and is designed to assist urban municipalities in improving road maintenance and repair procedures.

Primary LanguageMermaidMIT LicenseMIT

Road-Damage-Detection

Sections

1. Data Preprocessing

Given the information provided, here's a more detailed plan to train a CNN model for road damage detection:

1.1. Load Data (data_loader.py)

  • Functionality:
    • Load the annotations.json file.
    • Extract image paths, bounding box coordinates, and class labels from the JSON data.
    • Return a structured dataset, e.g., a list of dictionaries or a Pandas DataFrame.

1.2. Data Augmentation (data_augmentation.py)

  • Functionality:
    • Apply random transformations to the training images to increase the diversity of the dataset.
      • Rotations
      • Flips (horizontal and vertical)
      • Zooms (zoom in and out)
      • Brightness and contrast adjustments
    • Ensure that the bounding box annotations are adjusted accordingly to match the augmented images.
    • Return the augmented images and their corresponding annotations.

1.3. Data Splitting (data_splitter.py)

  • Functionality:
    • Since the dataset is already split into training and testing sets, this script will focus on creating a validation set.
    • Take a portion (e.g., 20%) of the training data and set it aside as the validation set.
    • Ensure that the split is stratified, i.e., the distribution of classes in the validation set is similar to that in the training set.
    • Return the training and validation datasets separately.

Each of these scripts (data_loader.py, data_augmentation.py, and data_splitter.py) will contain functions that can be imported and used in other parts of the project, such as the training script. This modular approach ensures that each preprocessing step is isolated, making it easier to modify or extend in the future.

1.1. Load Data

  • Load the annotations.json file.
  • Extract image paths, bounding box coordinates, and class labels from the JSON data.

1.2. Data Augmentation

  • Apply random rotations, flips, and zooms to the training images to increase the diversity of the dataset.
  • Adjust brightness and contrast to simulate different lighting conditions.

1.3. Data Splitting

  • Since the dataset is already split into training and testing sets, and the testing set doesn't have annotations, you can create a validation set by taking a portion (e.g., 20%) of the training data. This will help in tuning the model and preventing overfitting.

2. Model Architecture

2.1. Input Layer

  • Accepts road images of size 640x640x3 (RGB images).

2.2. Convolutional Layers

  • Multiple convolutional layers with increasing filter sizes to capture varying features.
  • Use ReLU activation functions.
  • Apply max-pooling after certain layers to reduce spatial dimensions.

2.3. Flattening Layer

  • Flatten the output from the convolutional layers to feed into the fully connected layers.

2.4. Fully Connected Layers

  • Dense layers to make decisions based on the features extracted by the convolutional layers.
  • Use dropout layers to prevent overfitting.

2.5. Output Layer

  • For object detection: Output bounding box coordinates and class scores.

3. Loss Function & Optimization

3.1. Loss Function

  • Use a combination of localization loss (for bounding box coordinates) and classification loss.

3.2. Optimizer

  • Use optimizers like Adam or SGD with momentum for training.

4. Training the Model

4.1. Batch Training

  • Train the model using mini-batches.

4.2. Validation

  • Monitor the model's performance on the validation set to prevent overfitting.

4.3. Model Checkpoints

  • Save the model weights at regular intervals or when the model achieves better performance on the validation set.

5. Evaluation

5.1. Metrics

  • For object detection: Mean Average Precision (mAP), Intersection over Union (IoU).

5.2. Test Set Evaluation

  • Since the test set doesn't have annotations, you won't be able to evaluate the model's performance on it. Instead, focus on the validation set for evaluation.

6. Post-processing

6.1. Non-maximum Suppression

  • To eliminate multiple bounding boxes for the same object, retain the box with the highest confidence score and suppress others with significant overlap.

7. Deployment

7.1. Model Export

  • Convert the model to a format suitable for deployment (e.g., TensorFlow Lite, ONNX).
deployment/export_model.py

This file will contain functions or scripts to export the trained model in a format suitable for deployment. This might include converting the model to TensorFlow's SavedModel format or another format like ONNX, depending on the deployment target.

import tensorflow as tf

def export_saved_model(model, export_path):
    """
    Export the trained model to TensorFlow's SavedModel format.

    Args:
    - model (tf.keras.Model): Trained Keras model.
    - export_path (str): Path to save the exported model.
    """
    model.save(export_path)

7.2. Integration

  • Integrate the model into the desired application/platform (e.g., mobile app, web service).

6.2. Deployment Strategies

This section would be more of a documentation or discussion section, detailing the chosen deployment strategy, considerations, and rationale. It might not have associated code in the directory but could be documented in README files or dedicated documentation.

6.3. Integration

deployment/api.py

This file will contain the code to serve the model via an API, allowing other systems or applications to access the model's predictions.

Using Flask as a simple web framework:

from flask import Flask, request, jsonify
import tensorflow as tf

app = Flask(__name__)

# Load the exported model
model = tf.keras.models.load_model('path_to_saved_model')

@app.route('/predict', methods=['POST'])
def predict():
    data = request.json
    # Preprocess the input data if necessary
    # ...
    predictions = model.predict(data)
    # Postprocess the predictions if necessary
    # ...
    return jsonify(predictions.tolist())

if __name__ == '__main__':
    app.run(debug=True)

6.4. Monitoring and Maintenance

Monitoring and maintenance are crucial when deploying machine learning models to ensure they continue to perform as expected in real-world scenarios. Monitoring helps in identifying any drifts in data or performance, while maintenance ensures that the model is updated as needed.

Monitoring

  1. Logging: Log every prediction request and response. This helps in tracking the model's usage and can be useful for debugging.

  2. Performance Metrics: Monitor the model's latency, throughput, and resource usage (CPU, memory). This helps in ensuring that the model meets the required service level agreements (SLAs).

  3. Model Performance: Track the model's accuracy, precision, recall, etc., in the real-world scenario. This can be done by periodically evaluating the model on new data.

  4. Data Drift: Monitor for changes in the input data distribution. If the data starts to drift significantly from the training data, it might be an indication that the model needs retraining.

  5. Alerts: Set up alerts for any anomalies, like a sudden drop in performance, increased latency, or system resource constraints.

Maintenance

  1. Model Retraining: Periodically retrain the model with new data to ensure it remains up-to-date.

  2. Model Versioning: Keep track of different versions of the model. This helps in rolling back to a previous version if needed.

  3. A/B Testing: Before deploying a new version of the model, test it alongside the current version to compare their performances.

  4. Backup and Recovery: Regularly back up the model and related data to ensure a quick recovery in case of failures.

6.5. Documentation and User Guide

Documentation is crucial for both developers and end-users to understand how to use and integrate with the deployed model.

  1. API Documentation:

    • Describe the API endpoints, request format, and response format.
    • Provide examples of API requests and responses.

  2. Model Details:

    • Describe the model architecture, training data, and performance metrics.
    • Provide details about the model's version, date of training, and any other relevant metadata.

  3. Integration Guide:

    • Provide step-by-step instructions for developers on how to integrate with the model API.
    • Include code samples and best practices.

  4. User Guide:

    • For non-developer users, provide a guide on how to use the model, perhaps through a user interface or application.
    • Include screenshots, FAQs, and troubleshooting steps.

  5. Maintenance Guide:

    • Document the procedures for monitoring, updating, and maintaining the deployed model.
    • Include details about alert systems, backup procedures, and retraining schedules.

For the directory structure:

  • Monitoring and Maintenance: This would be more of a documentation or discussion section. It might not have associated code in the directory you provided but could involve integrating with monitoring tools or platforms. If there's any code or scripts related to monitoring, they might reside in a monitoring/ directory.

  • Documentation and User Guide: This would primarily be in the form of README files, dedicated documentation pages, or inline comments in the code. A docs/ directory at the root level could contain detailed documentation, user guides, and other related materials.

8. Conclusion & Future Work

8.1. Summarize

  • Summarize the results, challenges faced, and lessons learned.

8.2. Potential Improvements

  • Discuss potential improvements and extensions for the model, such as using a pre-trained model for transfer learning or exploring different architectures.

9. References

  • List relevant papers, articles, and resources used during the project.

This plan provides a comprehensive approach to building and deploying a CNN for road damage detection using the provided dataset.