ResEViT-Road: An Efficient Model for Road Quality Classification

Nowadays, road infrastructure plays a critical role in economic development in each country. However, many roads in the world are facing bad quality due to various types of damage, including longitudinal cracks, transverse cracks, grid cracks, and potholes. This paper presents a novel model, namely ResEViT-Road, designed to accurately classify road quality based on ResNet and EfficientViT backbones. Our proposed architecture strikes a balance between convolutions and self-attention mechanisms, resulting in a simple yet efficient model. We propose an Interactive block that enhances the feature representation by transferring information while training between CNN and Transformer branches. As a result, ResEViT-Road is able to learn features more comprehensively and effectively, leading to superior classification results with F1-score reaching 86.05% for binary classification and 98.51% for multi-class classification tasks, in that order.