AI-Based Verification of Tyre Wear and Tear Using MobileNet-Based Image Classification

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

This research manuscript presents a proposed artificial intelligence-based approach for verifying tyre wear and tear using image classification and a MobileNet-based deep learning model. The objective is to explore how computer vision can support the identification of tyre wear conditions and assist vehicle maintenance and road-safety practices.The proposed workflow includes collecting tyre images, preparing and preprocessing the image dataset, training a MobileNet-based classification model, and evaluating its performance using appropriate classification metrics. The approach is intended to provide a foundation for a practical tyre inspection support system.The manuscript discusses the background, proposed methodology, potential applications, evaluation plan, limitations, and future scope of AI-based tyre wear detection. Reliable evaluation requires access to the original dataset, trained model, and test results. Since verified experimental results are not currently available, this manuscript does not claim a measured accuracy or experimentally validated performance.The work is relevant to automotive engineering, computer vision, deep learning, vehicle maintenance, and intelligent inspection systems.