Real-Time Tire Wear Prediction Using Deep Learning and Explainable AI

This research introduces a novel deep-learning model for tire wear detection that significantly enhances detection accuracy and system explainability. By integrating the Exception architecture with a custom Dual Attention layer, the model offers precise insights into tire wear patterns, focusing on critical features for improved predictive accuracy. The addition of Integrated Gradients for explainability not only elucidates the model's decision-making process but also fosters user trust by making the results understandable and transparent. The implementation includes a user-friendly interface that allows for real-time feedback and actionable insights, making it a practical tool for proactive vehicle maintenance and safety enhancements.