A Hybrid Approach for Pavement Crack Detection Using Mask R-CNN and Vision Transformer Model

Detecting pavement cracks is critical for road safety and infrastructure management. Traditional methods, relying on manual inspection and basic image processing, are time-consuming and prone to errors. Recent deep-learning (DL) methods automate crack detection, but many still struggle with variable crack patterns and environmental conditions. This study aims to address these limitations by introducing the MaskerTransformer, a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network (Mask R-CNN) with the global contextual awareness of Vision Transformer (ViT). The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions. We evaluated the performance of the MaskerTransformer against other state-of-the-art models such as U-Net, Transformer U-Net (TransUNet), U-Net Transformer (UNETr), Swin U-Net Transformer (Swin-UNETr), You Only Look Once version 8 (YoloV8), and Mask R-CNN using two benchmark datasets: Crack500 and DeepCrack. The findings reveal that the MaskerTransformer significantly outperforms the existing models, achieving the highest Dice Similarity Coefficient (DSC), precision, recall, and F1-Score across both datasets. Specifically, the model attained a DSC of 80.04% on Crack500 and 91.37% on DeepCrack, demonstrating superior segmentation accuracy and reliability. The high precision and recall rates further substantiate its effectiveness in real-world applications, suggesting that the MaskerTransformer can serve as a robust tool for automated pavement crack detection, potentially replacing more traditional methods.