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

Computers, Materials & Continua 2025年第1期82卷 561-577页

作者： shorouq alshawabkeh Li Wu Daojun Dong Yao Cheng Liping Li Faculty of Engineering China University of GeosciencesWuhan430074China

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.

关键词： Pavement crack segmentation transportation deep learning vision transformer Mask R-CNN image segmentation

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Automated Pavement Crack Detection Using Deep Feature Selection and Whale Optimization Algorithm

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Computers, Materials & Continua 2023年第10期77卷 63-77页

作者： shorouq alshawabkeh Li Wu Daojun Dong Yao Cheng Liping Li Mohammad Alanaqreh Faculty of Engineering China University of GeosciencesWuhan430074China

Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance *** advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains *** this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for *** performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation ***,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two *** results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection *** applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of *** results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement *** leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance ***,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.

关键词： Pavement crack detection deep learning feature selection whale optimization algorithm civil engineering

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