Pavement Crack Segmentation Using an Attention-Based Deep Learning Model

Paper

Dao, Hieu, Khuc, Tung, Truong, Quan, Dinh, Cang and Nguyen, Andy. 2024. "Pavement Crack Segmentation Using an Attention-Based Deep Learning Model." 4th International Conference on Sustainability in Civil Engineering (ICSCE 2022). Hanoi, Vietnam 25 - 27 Nov 2022 Singapore . Springer. https://doi.org/10.1007/978-981-99-2345-8_75
Paper/Presentation Title

Pavement Crack Segmentation Using an Attention-Based Deep Learning Model

Presentation TypePaper
AuthorsDao, Hieu, Khuc, Tung, Truong, Quan, Dinh, Cang and Nguyen, Andy
Journal or Proceedings TitleProceedings of the 4th International Conference on Sustainability in Civil Engineering
Journal Citation344, pp. 727-737
Number of Pages11
Year2024
PublisherSpringer
Place of PublicationSingapore
ISBN9789819923441
9789819923472
9789819923458
Digital Object Identifier (DOI)https://doi.org/10.1007/978-981-99-2345-8_75
Web Address (URL) of Paperhttps://link.springer.com/chapter/10.1007/978-981-99-2345-8_75
Web Address (URL) of Conference Proceedingshttps://link.springer.com/book/10.1007/978-981-99-2345-8
Conference/Event4th International Conference on Sustainability in Civil Engineering (ICSCE 2022)
Event Details
4th International Conference on Sustainability in Civil Engineering (ICSCE 2022)
Delivery
In person
Event Date
25 to end of 27 Nov 2022
Event Location
Hanoi, Vietnam
AbstractIt has been observed that cracks, the most common sign of deterioration happing on the pavement, are difficult to detect at an early stage. Although the U-net-based model has detected well-established cracks, it shows some limitations when working with low-quality pavement images that are automatically captured by moving pavement-inspection vehicles. In this study, the attention technique is applied to the U-net model to enhance the results of pavement crack detection under some difficult pavement image conditions. Attention gates (AGs) are deployed at the skip connections of the U-net model to remove irrelevant regions by setting attention weights for each image part. This procedure helps the U-net model learn how to eliminate extraneous regions in the input image. Therefore, the technique minimizes the computational resources by ignoring wasted irrelevant operations and enhances crack segmentation results. The proposed model is verified using a real-life image packet of pavement. The performance of the attention U-net model illustrates better outcomes compared to the ones from the U-net model.
KeywordsAttention gate
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020400505. Construction materials
SeriesLecture Notes in Civil Engineering
Byline AffiliationsHanoi University of Civil Engineering, Vietnam
School of Engineering
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