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

• URL: http://arxiv.org/abs/2601.13059v1
• Date: Mon, 19 Jan 2026 13:48:26 GMT
• Title: Prototype Learning-Based Few-Shot Segmentation for Low-Light Crack on Concrete Structures
• Authors: Yulun Guo,
• Abstract summary: Crack detection is critical for concrete infrastructure safety, but real-world cracks often appear in low-light environments like tunnels and bridge undersides.<n>We propose a dual-branch prototype learning network integrating Retinex theory with few-shot learning for low-light crack segmentation.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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