From Classification to Segmentation with Explainable AI: A Study on
  Crack Detection and Growth Monitoring
Florent Forest Hugo Porta Devis Tuia Olga Fink
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.