A Dataset for Visual Classification of Flat Tires

Flat tires pose significant safety risks, cause substantial energy loss, and can lead to structural damage to the wheel system if driven on for extended distances without detection. Traditional tire pressure monitoring systems are costly and may suffer from sensor malfunctions. A cost-effective visual detection method offers a competitive alternative for automated tire health monitoring. In this study, we developed a visual dataset of flat tires. The primary application of this dataset is to enable the development of a machine learning–based visual classification system capable of accurately detecting flat tires at an early stage. The intended outcome is a deployable classification system that enhances tire safety through rapid visual identification of hazardous conditions.