ResNet50-Driven Insights: A Framework for Forecasting Tyre Friction Damage

The significance of a vehicle's tyres cannot be overstated, as they directly affect performance. However, tyres often suffer from neglect in terms of proper inspection and maintenance. This widespread public disregard for tyre condition leads to excessive tread wear and sidewall damage, resulting in severe long-term consequences if unaddressed. Many accidents, particularly on highways and during rainy seasons, are linked to worn-out or damaged tyres. Despite this, many people struggle to distinguish between good and worn-out tyres, increasing the risk of using unsafe ones. Traditionally, tyre wear assessment has relied on manual inspection, but this method's limitations have led to the proposal of image processing-based solutions. This research revealed that ResNet50 accurately predicts tyre friction damage with high precision and efficiency, surpassing ResNet18 and GoogleNet. It enables real-time analysis and effective categorization of tyre conditions, enhancing road safety and reducing the need for manual inspections by providing timely and accurate maintenance insights.