[1H3-J-13] AI application: medicine and healthcare

[1H3-J-13-03] Eye-inspection Image-based Transfer Learning Rebar Expose using Semantic Segmentation

Michihiro Nakajima¹, 〇Takato Yasuno¹, Daisuke Nagatomi¹, Kazuhiro Noda¹, Kiyoshi Aoyanagi¹, Seiji Sekiguchi¹ (1. Yachiyo Engineering, Co.,Ltd.)

Keywords:Deep Learning, Semantic Segmentation, Transfer Learning, Infra Damage Detection, Rebar Expose

When civil infrastructures have been deteriorated, efficient and accurate diagnosis are required. 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. Generally speaking, rebar exposure is frequently occurred, and there are many opportunities to judge repair measures. This paper proposes three damage detection methods of transfer learning which enables semantic segmentation in an image with low pixels using damaged photos of eye-vision inspection. In fact, we show the results applied this method using the rebar exposed images on the real bridges.