A comparison study of semantic segmentation networks for crack detection in construction materials

Construction and Building Materials, Год журнала: 2024, Номер 414, С. 134950 - 134950

Опубликована: Янв. 21, 2024

Язык: Английский

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Deep learning-based intelligent detection of pavement distress

Automation in Construction, Год журнала: 2024, Номер 168, С. 105772 - 105772

Опубликована: Сен. 17, 2024

Язык: Английский

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CNN-based network with multi-scale context feature and attention mechanism for automatic pavement crack segmentation

Automation in Construction, Год журнала: 2024, Номер 164, С. 105482 - 105482

Опубликована: Май 24, 2024

Язык: Английский

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Semantic segmentation and deep CNN learning vision-based crack recognition system for concrete surfaces: development and implementation

Signal Image and Video Processing, Год журнала: 2025, Номер 19(4)

Опубликована: Фев. 24, 2025

Язык: Английский

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Pavement Crack Detection Based on the Improved Swin-Unet Model

Buildings, Год журнала: 2024, Номер 14(5), С. 1442 - 1442

Опубликована: Май 16, 2024

Accurate pavement surface crack detection is crucial for analyzing survey data and the development of maintenance strategies. On basis Swin-Unet, this study develops improved Swin-Unet (iSwin-Unet) model with developed skip attention module residual Swin Transformer block. Based on channel mechanism, region can be better captured while feature channels assigned more weights. Taking advantage block, encoder architecture globally feature. Meanwhile, information efficiently exchanged. To verify performance proposed model, we compare training visualization results other three models, which are Transformer, Unet, respectively. Three public benchmarks (CFD, Crack500, CrackSC) have been adopted purpose training, validation, testing. test results, it found that iSwin-Unet achieves a significant increase in mF1 score, mPrecision, mRecall compared to existing thereby establishing its efficacy underlining advancements over current methodologies.

Язык: Английский

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Crack segmentation‐guided measurement with lightweight distillation network on edge device

Computer-Aided Civil and Infrastructure Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

Abstract Pavement crack measurement (PCM) is essential for automated, precise road condition assessment. However, balancing speed and accuracy on edge artificial intelligence (AI) mobile devices remains challenging. This paper proposes a real‐time PCM framework deployment, incorporating lightweight distillation network surface feature algorithm. Specifically, the proposed instance‐aware hybrid module combines feature‐based relation‐based knowledge distillation, leveraging instance‐related information efficient transfer from teacher to student networks, which results in more accurate segmentation model. Additionally, algorithm, based distance mapping relationships coordinate extraction, addresses issues with branching loss, enhancing efficiency. Real‐time was performed actual roads utilizing robot equipped an computing unit. The precision reached 84.37%, frame per second of 77.72. Compared ground truth, relative error average width ranged 6.42% 40.65%, while length varied between 1.48% 3.76%. These findings highlight feasibility assessment save maintenance costs.

Язык: Английский

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Enhancing pixel-level crack segmentation with visual mamba and convolutional networks

Automation in Construction, Год журнала: 2024, Номер 168, С. 105770 - 105770

Опубликована: Сен. 11, 2024

Язык: Английский

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A three-stage pavement image crack detection framework with positive sample augmentation

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 129, С. 107624 - 107624

Опубликована: Дек. 4, 2023

Язык: Английский

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Enhancing pavement crack segmentation via semantic diffusion synthesis model for strategic road assessment

Results in Engineering, Год журнала: 2024, Номер 23, С. 102745 - 102745

Опубликована: Авг. 18, 2024

Язык: Английский

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Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures

Results in Engineering, Год журнала: 2024, Номер 25, С. 103726 - 103726

Опубликована: Дек. 11, 2024

Язык: Английский

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