Coarse-to-fine crack cue for robust crack detection

TL;DR

This paper introduces CrackCue, a novel coarse-to-fine crack cue generation method that enhances the robustness and generalization of crack detection models by leveraging the thin structure property of cracks.

Contribution

CrackCue is a new plug-and-play approach that generates a robust crack cue using coarse-to-fine processing, improving generalization across unseen domains.

Findings

Significantly improves baseline crack detection methods.

Enhances robustness against complex backgrounds, shadows, and lighting.

Demonstrates superior generalization in extensive experiments.

Abstract

Crack detection is an important task in computer vision. Despite impressive in-dataset performance, deep learning-based methods still struggle in generalizing to unseen domains. The thin structure property of cracks is usually overlooked by previous methods. In this work, we introduce CrackCue, a novel method for robust crack detection based on coarse-to-fine crack cue generation. The core concept lies on leveraging the thin structure property to generate a robust crack cue, guiding the crack detection. Specifically, we first employ a simple max-pooling and upsampling operation on the crack image. This results in a coarse crack-free background, based on which a fine crack-free background can be obtained via a reconstruction network. The difference between the original image and fine crack-free background provides a fine crack cue. This fine cue embeds robust crack prior information which is unaffected by complex backgrounds, shadow, and varied lighting. As a plug-and-play method, we incorporate the proposed CrackCue into three advanced crack detection networks. Extensive experimental results demonstrate that the proposed CrackCue significantly improves the generalization ability and robustness of the baseline methods. The source code will be publicly available.