  Recently, social infrastructure is aging, and its predictive maintenance has
become important issue. To monitor the state of infrastructures, bridge
inspection is performed by human eye or bay drone. For diagnosis, primary
damage region are recognized for repair targets. But, the degradation at worse
level has rarely occurred, and the damage regions of interest are often narrow,
so their ratio per image is extremely small pixel count, as experienced 0.6 to
1.5 percent. The both scarcity and imbalance property on the damage region of
interest influences limited performance to detect damage. If additional data
set of damaged images can be generated, it may enable to improve accuracy in
damage region segmentation algorithm. We propose a synthetic augmentation
procedure to generate damaged images using the image-to-image translation
mapping from the tri-categorical label that consists the both semantic label
and structure edge to the real damage image. We use the Sobel gradient operator
to enhance structure edge. Actually, in case of bridge inspection, we apply the
RC concrete structure with the number of 208 eye-inspection photos that rebar
exposure have occurred, which are prepared 840 block images with size 224 by
224. We applied popular per-pixel segmentation algorithms such as the FCN-8s,
SegNet, and DeepLabv3+Xception-v2. We demonstrates that re-training a data set
added with synthetic augmentation procedure make higher accuracy based on
indices the mean IoU, damage region of interest IoU, precision, recall, BF
score when we predict test images.
