  Efficient inspection and accurate diagnosis are required for civil
infrastructures with 50 years since completion. Especially in municipalities,
the shortage of technical staff and budget constraints on repair expenses have
become a critical problem. If we can detect damaged photos automatically
per-pixels from the record of the inspection record in addition to the 5-step
judgment and countermeasure classification of eye-inspection vision, then it is
possible that countermeasure information can be provided more flexibly, whether
we need to repair and how large the expose of damage interest. A piece of
damage photo is often sparse as long as it is not zoomed around damage, exactly
the range where the detection target is photographed, is at most only 1%.
Generally speaking, rebar exposure is frequently occurred, and there are many
opportunities to judge repair measure. In this paper, we propose three damage
detection methods of transfer learning which enables semantic segmentation in
an image with low pixels using damaged photos of human eye-inspection. Also, we
tried to create a deep convolutional network from scratch with the
preprocessing that random crops with rotations are generated. In fact, we show
the results applied this method using the 208 rebar exposed images on the 106
real-world bridges. Finally, future tasks of damage detection modeling are
mentioned.
