Výsledky vyhledávání - "crack segmentation"

Akademický článek

Dadnet: dual-attention detection network for crack segmentation on tomb murals

Autor: Meng Wu, Ruochang Chai, Yongqin Zhang, Zhiyong Lu

Publikováno v: Heritage Science, Vol 12, Iss 1, Pp 1-12 (2024)

Abstract Many tomb murals have punctate losses, cracks, and craquelure due to underground subsidence and changes in their physical support. Visual non-destructive detection techniques enable rapid assessment of how much tomb murals are affected by cr

Externí odkaz: https://doaj.org/article/f61d28f7f52c42839ff885e301c6553a

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Akademický článek

Crack SAM: enhancing crack detection utilizing foundation models and Detectron2 architecture

Autor: R Rakshitha, S Srinath, N Vinay Kumar, S Rashmi, B V Poornima

Publikováno v: Journal of Infrastructure Preservation and Resilience, Vol 5, Iss 1, Pp 1-19 (2024)

Abstract Accurate crack detection is crucial for maintaining pavement integrity, yet manual inspections remain labor-intensive and prone to errors, underscoring the need for automated solutions. This study proposes a novel crack segmentation approach

Externí odkaz: https://doaj.org/article/d694a3ffd4e547e3bdc245787d6e1d55

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Akademický článek

A UAV Based Concrete Crack Detection and Segmentation Using 2-Stage Convolutional Network with Transfer Learning

Autor: Joses Sorilla, Timothy Scott C. Chu, Alvin Y. Chua

Publikováno v: HighTech and Innovation Journal, Vol 5, Iss 3, Pp 690-702 (2024)

This study explores a non-destructive testing (NDT) method for crack detection using a two-stage convolutional neural network (CNN) model, incorporating a combination of AlexNet and YOLO models through transfer learning. Crack detection is pivotal fo

Externí odkaz: https://doaj.org/article/0379ed24267346849413834402e03620

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Akademický článek

Transfer learning-based encoder-decoder model with visual explanations for infrastructure crack segmentation: New open database and comprehensive evaluation

Autor: Fangyu Liu, Wenqi Ding, Yafei Qiao, Linbing Wang

Publikováno v: Underground Space, Vol 17, Iss , Pp 60-81 (2024)

Contemporary demands necessitate the swift and accurate detection of cracks in critical infrastructures, including tunnels and pavements. This study proposed a transfer learning-based encoder-decoder method with visual explanations for infrastructure

Externí odkaz: https://doaj.org/article/9b69b794911b4a60966eadeae2f1a603

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Akademický článek

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

Autor: Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

Publikováno v: Results in Engineering, Vol 25, Iss , Pp 103726- (2025)

Automated pavement crack detection faces significant challenges due to the complex shapes of crack patterns, their similarity to non-crack textures, and varying environmental conditions such as lighting and noise. Traditional methods often struggle t

Externí odkaz: https://doaj.org/article/52fc81509f9c427180d060a689767aac

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Akademický článek

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

Autor: Saúl Cano-Ortiz, Eugenio Sainz-Ortiz, Lara Lloret Iglesias, Pablo Martínez Ruiz del Árbol, Daniel Castro-Fresno

Publikováno v: Results in Engineering, Vol 23, Iss , Pp 102745- (2024)

Computer-aided deep learning has significantly advanced road crack segmentation. However, supervised models face challenges due to limited annotated images. There is also a lack of emphasis on deriving pavement condition indices from predicted masks.

Externí odkaz: https://doaj.org/article/fe368c2e8bb0435abb56ecd66d3b367e

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