Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations. Forest, F., Porta, H., Tuia, D., & Fink, O. In Proceedings of the 33rd European Safety and Reliability Conference (ESREL), pages 1392–1393, 2023. ![Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations [link]](https://bibbase.org/img/filetypes/link.svg "link")
Link ![Segmenting Without Annotating: Crack Segmentation and Monitoring via Post-Hoc Classifier Explanations [pdf]](https://bibbase.org/img/filetypes/pdf.svg "pdf")
Paper doi abstract bibtex Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe health monitoring, especially in hard-to-reach locations. To this aim, data-driven approaches based on machine learning have demonstrated their effectiveness, at the expense of annotating large sets of images for supervised training. Once a damage has been detected, one also needs to monitor the evolution of its severity, in order to trigger a timely maintenance operation and avoid any catastrophic consequence. This evaluation requires a precise segmentation of the damage. However, pixel-level annotation of images for segmentation is labor-intensive. On the other hand, labeling images for a classification task is relatively cheap in comparison. To circumvent the cost of annotating images for segmentation, recent works inspired by explainable AI (XAI) have proposed to use the post-hoc explanations of a classifier to obtain a segmentation of the input image. In this work, we study the application of XAI techniques to the detection and monitoring of cracks in masonry wall surfaces. We benchmark different post-hoc explainability methods in terms of segmentation quality and accuracy of the damage severity quantification (for example, the width of a crack), thus enabling timely decision-making.
@inproceedings{forest2023segmenting,
title = {Segmenting {Without} {Annotating}: {Crack} {Segmentation} and {Monitoring} via {Post}-{Hoc} {Classifier} {Explanations}},
copyright = {All rights reserved},
isbn = {978-981-18807-1-1},
shorttitle = {Segmenting {Without} {Annotating}},
doi = {10.3850/978-981-18-8071-1_P290-cd},
abstract = {Monitoring the cracks in walls, roads and other types of infrastructure is essential to ensure the safety of a structure, and plays an important role in structural health monitoring. Automatic visual inspection allows an efficient, costeffective and safe health monitoring, especially in hard-to-reach locations. To this aim, data-driven approaches based on machine learning have demonstrated their effectiveness, at the expense of annotating large sets of images for supervised training. Once a damage has been detected, one also needs to monitor the evolution of its severity, in order to trigger a timely maintenance operation and avoid any catastrophic consequence. This evaluation requires a precise segmentation of the damage. However, pixel-level annotation of images for segmentation is labor-intensive. On the other hand, labeling images for a classification task is relatively cheap in comparison. To circumvent the cost of annotating images for segmentation, recent works inspired by explainable AI (XAI) have proposed to use the post-hoc explanations of a classifier to obtain a segmentation of the input image. In this work, we study the application of XAI techniques to the detection and monitoring of cracks in masonry wall surfaces. We benchmark different post-hoc explainability methods in terms of segmentation quality and accuracy of the damage severity quantification (for example, the width of a crack), thus enabling timely decision-making.},
language = {en},
booktitle = {Proceedings of the 33rd {European} {Safety} and {Reliability} {Conference} (ESREL)},
author = {Forest, Florent and Porta, Hugo and Tuia, Devis and Fink, Olga},
year = {2023},
pages = {1392--1393},
url_Link = {https://www.rpsonline.com.sg/proceedings/esrel2023/html/P290.html},
url_Paper = {https://www.rpsonline.com.sg/proceedings/esrel2023/pdf/P290.pdf},
}
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