A Sustainable Approach to Waste Management of Tyres: Using Artificial Intelligence for Enhanced Accuracy

This study aims to develop a model that can detect and classify tyres, distinguishing between defective and functional ones to aid in quality inspection and support sustainable waste management practices. The study adopted a structured approach to developing an automated system for classifying tyre quality, using ResNet-50 and YOLO (You Only Look Once) for real-time detection. The precision peaks at 0.9552, indicating excellent performance of the model. The study's findings enforce the potential of deep learning models to increase efficiency and safety within the automotive sector, particularly in areas like preventive maintenance and tyre recycling. The quality control and enhancement in waste management practices of tyres within the automotive sector can be achieved by integrating real-time detection and the precise classification of tyres using this model.