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

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.