# Weakly-Supervised Crack Detection

arXiv: https://arxiv.org/abs/2206.06743 · HTML: https://mlsanity.com/paper/2206.06743/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 38 references that are in the map.
- First posted on arXiv 14 Jun 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Albumentations: fast and flexible image augmentations](https://mlsanity.com/paper/1809.06839/) (2018)
- [What's the Point: Semantic Segmentation with Point Supervision](https://mlsanity.com/paper/1506.02106/) (2015)
- [Weakly Supervised Learning of Instance Segmentation with Inter-pixel Relations](https://mlsanity.com/paper/1904.05044/) (2019)
- [Simple Does It: Weakly Supervised Instance and Semantic Segmentation](https://mlsanity.com/paper/1603.07485/) (2016)
- [Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation](https://mlsanity.com/paper/1603.06098/) (2016)
- [Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection](https://mlsanity.com/paper/1901.06340/) (2019)
- [SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2004.03696/) (2020)
- [Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding](https://mlsanity.com/paper/1904.08582/) (2019)
- [Automatic Pavement Crack Detection Based on Structured Prediction with the Convolutional Neural Network](https://mlsanity.com/paper/1802.02208/) (2018)
- [Unsupervised Pixel-level Road Defect Detection via Adversarial Image-to-Frequency Transform](https://mlsanity.com/paper/2001.11175/) (2020)
- [NHA12D: A New Pavement Crack Dataset and a Comparison Study Of Crack Detection Algorithms](https://mlsanity.com/paper/2205.01198/) (2022)
- [Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and Thresholding](https://mlsanity.com/paper/2109.00456/) (2021)
- [Crack Detection as a Weakly-Supervised Problem: Towards Achieving Less Annotation-Intensive Crack Detectors](https://mlsanity.com/paper/2011.02208/) (2020)

## Built on by (most cited first, 1 of 1)

- [Rapid-Deployment Crack Measurement Based on SAM3 Semantic-Edge Response Decoding](https://mlsanity.com/paper/2607.12292/) (2026)

## Abstract (arXiv)

Pixel-level crack segmentation is widely studied due to its high impact on building and road inspections. While recent studies have made significant improvements in accuracy, they typically heavily depend on pixel-level crack annotations, which are time-consuming to obtain. In earlier work, we proposed to reduce the annotation cost bottleneck by reformulating the crack segmentation problem as a weakly-supervised problem -- i.e. the annotation process is expedited by sacrificing the annotation quality. The loss in annotation quality was remedied by refining the inference with per-pixel brightness values, which was effective when the pixel brightness distribution between cracks and non-cracks are well separated, but struggled greatly for lighter-colored cracks as well as non-crack targets in which the brightness distribution is less articulated. In this work, we propose an annotation refinement approach which takes advantage of the fact that the regions falsely annotated as cracks have similar local visual features as the background. Because the proposed approach is data-driven, it is effective regardless of a dataset's pixel brightness profile. The proposed method is evaluated on three crack segmentation datasets as well as one blood vessel segmentation dataset to test for domain robustness, and the results show that it speeds up the annotation process by factors of 10 to 30, while the detection accuracy stays at a comparable level.