Prototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structures
This addresses the problem of infrastructure safety monitoring for engineers and inspectors by enabling accurate crack detection in challenging low-light conditions, though it is an incremental improvement over existing few-shot segmentation methods.
The paper tackles crack segmentation in low-light environments by proposing a dual-branch prototype learning network that integrates Retinex theory with few-shot learning, achieving state-of-the-art performance on multiple benchmarks.
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.