Study on image diagnosis of timber houses damaged by earthquake using deep learning

Image processing (semantic segmentation and morphological processing) for the image diagnosis of timber houses damaged by earthquake was studied and the following three aspects were revealed. Comparing the performance of the models trained with real datasets and chromakeyed datasets, the validation correctness of chromakeyed models achieved comparable or better accuracy than real models (up to 1.38 times more accurate in Frequency Weighted Intersection over Union ( FWIoU )). Thus, the usefulness of chromakeyed datasets was confirmed for creating image databases for deep learning. In the image diagnosis results, damage could be assessed with the same accuracy as the investigator if the damage rate was calculated to be 60% or more, unless the damage was unrecognizable due to shielding or resolution problems. This implies the feasibility of image diagnosis for quantitative damage assessment of timber houses. Considering the installation of the estimation methodology of the deformation angle into the image diagnosis, the guideline methodology, and previous experimental values were analyzed. If multiple damage degrees were detected, it could be narrowed down to limited estimation results. This suggests the possibility of installation of estimation methodology into the image diagnosis.