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Corrosion detection on metal constructions is a major challenge in civilengineering for quick, safe and effective inspection. Existing image analysisapproaches tend to place bounding boxes around the defected region which is notadequate both for structural analysis and pre-fabrication, an innovativeconstruction concept which reduces maintenance cost, time and improves safety.In this paper, we apply three semantic segmentation-oriented deep learningmodels (FCN, U-Net and Mask R-CNN) for corrosion detection, which performbetter in terms of accuracy and time and require a smaller number of annotatedsamples compared to other deep models, e.g. CNN. However, the final imagesderived are still not sufficiently accurate for structural analysis andpre-fabrication. Thus, we adopt a novel data projection scheme that fuses theresults of color segmentation, yielding accurate but over-segmented contours ofa region, with a processed area of the deep masks, resulting in high-confidencecorroded pixels.

Pixel-level Corrosion Detection on Metal Constructions by Fusion of Deep Learning Semantic and Contour Segmentation