Deep Learning-Based Classification of Road Conditions Using A Customized VGG-Style CNN For Pothole Detection

The detection of potholes in asphalt has been a concern in the field of deep learning to identify patterns with the best possible accuracy. This research presents a robust deep learning-based approach for classifying road conditions into "Normal" and "Pothole" categories using a customized convolutional neural network (CNN). The proposed model employs a VGG-style architecture designed to capture spatial hierarchies and effectively distinguish between the two classes. Data augmentation techniques, including random rotations and translations, are applied to enhance the diversity of training data and improve the model's generalization capabilities. The dataset, comprising images from real-world scenarios, is preprocessed and split into training and validation sets. The model is trained using the Adam optimizer with carefully tuned hyperparameters. Experimental results demonstrate a high validation accuracy, supported by precision, recall, and F1-score metrics, highlighting the effectiveness of the approach. A confusion matrix analysis provides further insights into the model's classification performance. This framework can serve as a reliable tool for automated road condition assessment, potentially aiding in proactive maintenance and road safety improvements.