# From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring

arXiv: https://arxiv.org/abs/2309.11267 · HTML: https://mlsanity.com/paper/2309.11267/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 79 references that are in the map.
- First posted on arXiv 20 Sep 2023.

## Builds on (most cited first, 22 of 22)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Axiomatic Attribution for Deep Networks](https://mlsanity.com/paper/1703.01365/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Learning Important Features Through Propagating Activation Differences](https://mlsanity.com/paper/1704.02685/) (2017)
- [Transformer Interpretability Beyond Attention Visualization](https://mlsanity.com/paper/2012.09838/) (2020)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [Top-down Neural Attention by Excitation Backprop](https://mlsanity.com/paper/1608.00507/) (2016)
- [Maxout Networks](https://mlsanity.com/paper/1302.4389/) (2013)
- [B-cos Networks: Alignment is All We Need for Interpretability](https://mlsanity.com/paper/2205.10268/) (2022)
- [TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation](https://mlsanity.com/paper/1801.05746/) (2018)
- [Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy](https://mlsanity.com/paper/2106.13200/) (2021)
- [Convolutional Dynamic Alignment Networks for Interpretable Classifications](https://mlsanity.com/paper/2104.00032/) (2021)
- [Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding](https://mlsanity.com/paper/1904.08582/) (2019)
- [CrackSeg9k: A Collection and Benchmark for Crack Segmentation Datasets and Frameworks](https://mlsanity.com/paper/2208.13054/) (2022)
- [What You See is What You Classify: Black Box Attributions](https://mlsanity.com/paper/2205.11266/) (2022)
- [GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network](https://mlsanity.com/paper/1705.07999/) (2017)
- [From Explanations to Segmentation: Using Explainable AI for Image Segmentation](https://mlsanity.com/paper/2202.00315/) (2022)
- [A Deep Neural Networks Approach for Pixel-Level Runway Pavement Crack Segmentation Using Drone-Captured Images](https://mlsanity.com/paper/2001.03257/) (2020)
- [Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification](https://mlsanity.com/paper/2206.05148/) (2022)

## Built on by (most cited first, 4 of 4)

- [Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey](https://mlsanity.com/paper/2405.01636/) (2024)
- [Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers](https://mlsanity.com/paper/2507.19168/) (2025)
- [No Masks Needed: Explainable AI for Deriving Segmentation from Classification](https://mlsanity.com/paper/2508.04534/) (2025)
- [Weakly Supervised Polar Low Segmentation in Sentinel-1 SAR Imagery](https://mlsanity.com/paper/2608.14366/) (2026)

## Abstract (arXiv)

Monitoring surface cracks in infrastructure is crucial for structural health monitoring. Automatic visual inspection offers an effective solution, especially in hard-to-reach areas. Machine learning approaches have proven their effectiveness but typically require large annotated datasets for supervised training. Once a crack is detected, monitoring its severity often demands precise segmentation of the damage. However, pixel-level annotation of images for segmentation is labor-intensive. To mitigate this cost, one can leverage explainable artificial intelligence (XAI) to derive segmentations from the explanations of a classifier, requiring only weak image-level supervision. This paper proposes applying this methodology to segment and monitor surface cracks. We evaluate the performance of various XAI methods and examine how this approach facilitates severity quantification and growth monitoring. Results reveal that while the resulting segmentation masks may exhibit lower quality than those produced by supervised methods, they remain meaningful and enable severity monitoring, thus reducing substantial labeling costs.