# Segment Any Crack: Deep Semantic Segmentation Adaptation for Crack Detection

arXiv: https://arxiv.org/abs/2504.14138 · HTML: https://mlsanity.com/paper/2504.14138/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 19 Apr 2025.

## Builds on (most cited first, 13 of 13)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning](https://mlsanity.com/paper/2210.08823/) (2022)
- [Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Features in CNNs](https://mlsanity.com/paper/2003.00152/) (2020)
- [Tuning LayerNorm in Attention: Towards Efficient Multi-Modal LLM Finetuning](https://mlsanity.com/paper/2312.11420/) (2023)
- [Parameter-Efficient Fine-Tuning for Foundation Models](https://mlsanity.com/paper/2501.13787/) (2025)
- [Fine-tuning vision foundation model for crack segmentation in civil infrastructures](https://mlsanity.com/paper/2312.04233/) (2023)
- [Real-time High-Resolution Neural Network with Semantic Guidance for Crack Segmentation](https://mlsanity.com/paper/2307.00270/) (2023)
- [Fast and Accurate Road Crack Detection Based on Adaptive Cost-Sensitive Loss Function](https://mlsanity.com/paper/2106.15510/) (2021)

## Built on by (most cited first, 3 of 3)

- [AtlasPatch: Scalable Foundation Model-based Tissue Detection and Patch Extraction for Computational Pathology](https://mlsanity.com/paper/2602.03998/) (2026)
- [Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasets](https://mlsanity.com/paper/2508.10256/) (2025)
- [Rethinking Efficient Crack Segmentation with Task-Aligned Structural-Directional Modeling](https://mlsanity.com/paper/2605.31048/) (2026)

## Abstract (arXiv)

Image-based crack detection algorithms are increasingly in demand in infrastructure monitoring, as early detection of cracks is of paramount importance for timely maintenance planning. While deep learning has significantly advanced crack detection algorithms, existing models often require extensive labeled datasets and high computational costs for fine-tuning, limiting their adaptability across diverse conditions. This study introduces an efficient selective fine-tuning strategy, focusing on tuning normalization components, to enhance the adaptability of segmentation models for crack detection. The proposed method is applied to the Segment Anything Model (SAM) and five well-established segmentation models. Experimental results demonstrate that selective fine-tuning of only normalization parameters outperforms full fine-tuning and other common fine-tuning techniques in both performance and computational efficiency, while improving generalization. The proposed approach yields a SAM-based model, Segment Any Crack (SAC), achieving a 61.22% F1-score and 44.13% IoU on the OmniCrack30k benchmark dataset, along with the highest performance across three zero-shot datasets and the lowest standard deviation. The results highlight the effectiveness of the adaptation approach in improving segmentation accuracy while significantly reducing computational overhead.