Road-Damage-Classification-Using-ResNet50-InceptionV3-and-VGG16-A-Deep-Learning-Approach

This project focuses on developing a robust multi-label classification system to identify and categorize various types of road damage. By leveraging pre-trained deep learning models—ResNet50, InceptionV3, and VGG16—we aim to automate the process of road damage detection from a dataset of 4000 TIF images, each labeled with 26 different damage types. The project explores the effectiveness of these models in accurately predicting damage severity on a scale of 0 to 5, enhancing road maintenance planning and resource allocation.

Objectives:

Develop a Multi-Label Classification System: Use state-of-the-art deep learning models to predict multiple road damage types simultaneously. Model Comparison: Evaluate the performance of ResNet50, InceptionV3, and VGG16 models, comparing their accuracy, precision, recall, and F2-score. Visualize Results: Provide insightful visualizations including loss curves, confusion matrices, and a comparison of training metrics across models.

Dataset:

• Images: 4000 TIF images of roads, each labeled with 26 damage types.
• Labels: Each damage type is rated on a scale of 0 to 5, indicating severity.

Methodology:

• Data Preprocessing:
• Extract and process the road images and labels.
• Normalize images and resize them to the required input sizes for each model.

Model Selection:

• ResNet50: Known for its deep residual architecture, which mitigates the vanishing gradient problem.
• InceptionV3: Utilizes auxiliary classifiers during training to improve gradient flow.
• VGG16: Characterized by its simplicity and depth, with 16 layers focusing on uniform convolutional layers.

Training and Evaluation:

• Implement custom training loops for each model, tracking loss and evaluation metrics.
• Use CrossEntropyLoss and Adam optimizer for effective training.
• Perform validation after each epoch and record accuracy, precision, recall, and F2-score.

Visualizations:

• Loss Curves: Plot training and validation loss to observe convergence.
• Confusion Matrices: Analyze model performance on individual damage types.
• Training Metrics Comparison: Visualize accuracy, precision, recall, and F2-score across epochs for all models.

Hyperparameter Tuning:

• Experiment with learning rates, batch sizes, and layer freezing to optimize model performance.

Deployment Potential:

• Discussed the application of the best-performing model in real-world scenarios, such as automated road inspections and maintenance scheduling.

Results:

The project successfully trained and evaluated three deep learning models, achieving varying degrees of success in road damage classification. ResNet50: Provided balanced performance across all metrics. InceptionV3: Excelled in precision and F2-score, benefiting from its auxiliary classifiers. VGG16: Delivered strong recall but required more computational resources due to its depth.

Conclusions:

• Model Efficacy: Each model has strengths and weaknesses, with InceptionV3 showing the most promise in terms of precision and overall F2-score.
• Real-World Application: The findings demonstrate the potential of deep learning in automating road damage detection, offering a scalable solution for infrastructure management.

Future Work:

• Unfreeze More Layers: Experiment with unfreezing additional layers in each model to potentially improve accuracy.
• Larger Datasets: Expand the dataset for training to improve model generalization.
• Deployment: Investigate the integration of the best-performing model into a mobile or web-based application for real-time road damage detection.

For access to the dataset , please email me at [email protected]