Deep learning による Semantic Segmentation を用いたコンクリート表面ひび割れの検出

抄録

Due to their wide applicability in inspection of concrete structures, there is considerable interest in the development of automated crack detection method by image processing. However, the accuracy of existing methods tends to be influenced by the existence of traces of tie-rod holes and formworks. In order to reduce these influences, this paper propose a crack detection method based on semantic segmentation by deep learning. The accuracy of developed method is investigated by the photos of concrete structures with lots of adverse conditions including shadow and dirt, and it is found that not only the crack region could be detected but also the trace of tie-rod holes and formworks could be removed from the detection result with high accuracy.