A Comprehensive Review of Hybrid Strategies and Deep Learning in Tyre Defect Detection

Abstract

Abstract: This comprehensive exploration centers on the pivotal role of tyres in vehicular performance, particularly in the era of autonomous vehicles. Navigating the intricate dynamics of tyre-road interactions, the study delves into a spectrum of methodologies for effective tyre defect detection. Emphasizing the evolutionary landscape from intelligent tyres with sensor technology to the transformative impact of deep learning, the focus remains on addressing challenges such as imbalanced datasets and limited labeled samples through strategies like transfer learning and domain adaptation. Key studies include innovative approaches like Hybrid-DBN for crack detection, 3D laser scanning for low-visibility defects, and the application of Wasserstein GAN for handling imbalanced datasets. These advancements signal paradigm shifts in the field, integrating hybrid methodologies, inventive data augmentation, and domain adaptation. Real-world scenarios, including tyre tread wear detection and MEMS accelerometer-based monitoring, highlight the practical implications of these methodologies. The conclusion acknowledges persistent challenges and suggests future research directions, advocating for standardized evaluation frameworks and collaborative efforts. The synthesis of insights contributes to the maturation of tyre defect detection research, offering a guide for the development of safer and more reliable vehicles on the road.