  Corrosion detection on metal constructions is a major challenge in civil
engineering for quick, safe and effective inspection. Existing image analysis
approaches tend to place bounding boxes around the defected region which is not
adequate both for structural analysis and pre-fabrication, an innovative
construction concept which reduces maintenance cost, time and improves safety.
In this paper, we apply three semantic segmentation-oriented deep learning
models (FCN, U-Net and Mask R-CNN) for corrosion detection, which perform
better in terms of accuracy and time and require a smaller number of annotated
samples compared to other deep models, e.g. CNN. However, the final images
derived are still not sufficiently accurate for structural analysis and
pre-fabrication. Thus, we adopt a novel data projection scheme that fuses the
results of color segmentation, yielding accurate but over-segmented contours of
a region, with a processed area of the deep masks, resulting in high-confidence
corroded pixels.
