Prototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structures

TL;DR

This paper introduces a dual-branch prototype learning network that combines Retinex theory and few-shot learning to effectively segment low-light cracks on concrete structures, reducing the need for extensive annotated datasets.

Contribution

The proposed method integrates Retinex-based illumination invariance with prototype and metric learning, introducing novel modules for high-dimensional similarity and multi-scale feature fusion in low-light crack segmentation.

Findings

Achieves state-of-the-art performance on multiple benchmarks.

Effectively segments cracks in low-light conditions with limited annotations.

Demonstrates robustness and accuracy across diverse datasets.

Abstract

Crack detection is critical for concrete infrastructure safety, but real-world cracks often appear in low-light environments like tunnels and bridge undersides, degrading computer vision segmentation accuracy. Pixel-level annotation of low-light crack images is extremely time-consuming, yet most deep learning methods require large, well-illuminated datasets. We propose a dual-branch prototype learning network integrating Retinex theory with few-shot learning for low-light crack segmentation. Retinex-based reflectance components guide illumination-invariant global representation learning, while metric learning reduces dependence on large annotated datasets. We introduce a cross-similarity prior mask generation module that computes high-dimensional similarities between query and support features to capture crack location and structure, and a multi-scale feature enhancement module that fuses multi-scale features with the prior mask to alleviate spatial inconsistency. Extensive experiments on multiple benchmarks demonstrate consistent state-of-the-art performance under low-light conditions. Code: https://github.com/YulunGuo/CrackFSS.