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

作     者：Shorouq Alshawabkeh Li Wu Daojun Dong Yao Cheng Liping Li 

作者机构：Faculty of EngineeringChina University of GeosciencesWuhan430074China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2025年第82卷第1期

页      面：561-577页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：Thanks to the reviewers for their valuable efforts and insightful comments to improve this paper quality and clarity 

主　　题：Pavement crack segmentation transportation deep learning vision transformer Mask R-CNN image segmentation 

摘      要：Detecting pavement cracks is critical for road safety and infrastructure *** methods,relying on manual inspection and basic image processing,are time-consuming and prone to *** deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental *** study aims to address these limitations by introducing the Masker Transformer,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 *** evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and *** findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both ***,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and *** high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.