An applied computer vision project focused on the automated detection of road surface damages such as potholes and cracks using deep learning–based object detection.
The project demonstrates an end-to-end pipeline for image-based road condition monitoring in real-world scenarios.
The project is implemented in Python using the PyTorch deep learning framework.
A YOLO-based object detection model is used for damage localization and classification.
Image processing and data handling are performed using OpenCV and NumPy, with experimentation carried out in a Jupyter Notebook environment.
Traditional road inspection methods are manual, time-consuming, and difficult to scale across large geographic regions.
This project aims to automatically detect and localize road damages from images, enabling efficient, consistent, and scalable infrastructure monitoring using deep learning techniques.
The project uses publicly available road damage image datasets (RDD-style datasets).
The dataset contains annotated images covering multiple damage categories, including potholes, longitudinal cracks, and transverse cracks.
1. Data Preprocessing
Images are resized and normalized, bounding-box annotations are parsed, and the dataset is split into training and validation sets.
2. Model Training
A YOLO-based object detection model is trained on custom road damage classes using supervised learning.
3. Evaluation
Model predictions are evaluated through visual inspection and basic performance analysis to assess detection quality.
The trained model is able to successfully detect common road damage patterns from static images.
The results demonstrate that deep learning–based object detection is a feasible and effective approach for automated road inspection.
Sample prediction outputs are available in the samples/ directory.
Model performance is sensitive to dataset size, class imbalance, and annotation quality.
Environmental factors such as lighting conditions, shadows, and camera angles can affect detection accuracy.
Additional tuning and validation are required before production-level deployment.
Planned improvements include training on larger and more diverse datasets, deploying the model as a REST API or edge-based application, supporting real-time video inference, and improving generalization through data augmentation and model optimization.
This project highlights the practical application of deep learning and computer vision for infrastructure monitoring and demonstrates how object detection models can support scalable and automated road damage assessment.