A Novel Deep Learning Model for End-to-End Characterization of Thin Cracking in SHCCs

Das, Avik Kumar; Leung, Christopher Kin Ying

doi:10.1007/978-3-031-15805-6_20

Cited by 1 publication

(1 citation statement)

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“…Therefore, visual technology is commonly used in this field. Edge detection involves fewer computational parameters, offering high real-time capability, but it struggles to accurately extract edge information from complex cracks, resulting in low accuracy [21,22]. With the continuous development of deep learning, it is gradually applied to inspection tasks in the field of concrete architecture.…”

Section: Introductionmentioning

confidence: 99%

DFP-Net: A Crack Segmentation Method Based on a Feature Pyramid Network

Li,

Liu,

Ali

et al. 2024

Applied Sciences

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Timely detection of defects is essential for ensuring safe and stable operation of concrete buildings. Automatic segmentation of concrete buildings’ surfaces is challenging due to the high diversity of crack appearance, the detailed information, and the unbalanced proportion of crack pixels and background pixels. In this work, the Double Feature Pyramid Network is designed for high-precision crack segmentation. Our work reached the state-of-the-art level in crack segmentation, with key contributions outlined as follows: firstly, considering the diversity of crack shapes, the network constructs a feature pyramid containing three feature extraction backbones to extract the global feature map with three scale input images. In particular, due to the biggest challenge being too much single-pixel crack area, the targeted feature pyramid based on the high-resolution is added to extract adequate shallow semantic information. Lastly, designing a cascade feature fusion unit to aggregate the extracted multi-dimensional feature maps and obtain the final prediction. Compared with existing crack detection methods, the superior performance of this method has been verified based on extensive experiments, with Pixel Accuracy of 65.99%, Intersection over Union of 44.71%, and Recall of 62.95%, providing a reliable and efficient solution for the health monitoring and maintenance of concrete structures. This work contributes to the advancement of research and practical applications in related fields, offering robust support for the monitoring and maintenance of concrete structures.

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Section: Introductionmentioning

confidence: 99%