Artificial Intelligence Techniques for Classification of Regular and Irregular Tire Wear

Tire condition is a critical factor for vehicle safety, influencing handling, braking efficiency, and accident prevention. Irregular wear patterns are not haphazard but are direct signals of certain mechanical defects, like improper wheel alignment, improper tire inflation, or suspension part malfunctions. Although qualified mechanics can identify these patterns, visual inspection is human-based and non-scalable. This study suggests an automated system for categorizing tire wear patterns based on artificial intelligence. This work employs a Convolutional Neural Network (CNN) architecture to learn tire images and categorize them into various categories of wear based on definitions in automotive engineering (center wear, shoulder wear, one-sided wear). The model further provides a diagnostic prediction by mapping the classified pattern of wear to its most probable cause. Our methodology involves creating a dataset of labeled tire images, data augmentation for increased model stability, and training a deep learning model to achieve high-accuracy classification. The result shows the potential of AI as a useful, accessible tool for proactive vehicle maintenance, enhancing safety and reducing costs for customers through early identification of latent car problems.