Implicit function‐based continuous representation for meticulous segmentation of cracks from high‐resolution images

Author:

Chu Honghu1,Long Lizhi1,Guo Jingjing1,Yuan Huaqing1,Deng Lu12

Affiliation:

1. College of Civil Engineering Hunan University Changsha China

2. Key Laboratory of Damage Diagnosis for Engineering Structures of Hunan Province Hunan University Changsha China

Abstract

AbstractHigh‐resolution (HR) crack images offer more detailed information for evaluating the structural condition and formulating effective maintenance or rehabilitation plans. However, the meticulous segmentation of HR crack images has been a challenge due to the limitations of mainstream deep learning algorithms that extract features in a discrete manner, as well as the constraints of computing resources. To address this issue, a novel implicit function‐integrated architecture, called the crack continuous refinement network (CCRN), was proposed for meticulous segmentation of cracks from HR images using a continuous representation manner. First, a crack continuous alignment module with a position encoding function was proposed to encode the tiny crack pixels that are easily lost in the sampling process. Then, a lightweight decoder embedded with implicit functions was customized to recover crack details from the aligned latent features and continuous position encoding information. Afterward, the gap between low‐resolution training images and HR inference results was bridged by the proposed continuous inference strategy. Finally, the robustness and practicability of the well‐trained CCRN were demonstrated by a parallel comparison and an unmanned aerial vehicle‐based field experiment.

Funder

National Natural Science Foundation of China

Hunan Provincial Innovation Foundation for Postgraduate

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Graphics and Computer-Aided Design,Computer Science Applications,Civil and Structural Engineering,Building and Construction

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A rendering‐based lightweight network for segmentation of high‐resolution crack images;Computer-Aided Civil and Infrastructure Engineering;2024-06-23

2. Fine‐grained crack segmentation for high‐resolution images via a multiscale cascaded network;Computer-Aided Civil and Infrastructure Engineering;2023-10-17

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