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

Abstract

Image processing (semantic segmentation and morphological processing) for the imavge diagnosis of timber houses damaged by earthquake was studied and the following three aspects were revealed.1 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. 2 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. 3 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. Keywords damage assessment, deep learning, image diagnosis, image processing, timber houseThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Structural Damage Detection Technique of Secondary Building Components Using Piezoelectric Sensors

“…In addition, the durations required for emergent inspections have been raised as a primary concern. In spite of enormous efforts by engineers and public servants, damage inspections after the 2016 Kumamoto earthquake took 57 days to complete [4]. One reason underlying this long period is that there were few engineers able to complete on-site inspections at the municipality level.…”

“…The recent development of image sensing has realized damage detection using digital images. Earlier achievements by Chida and Takahashi [4] enabled the detection and evaluation of quantitative damage at the ground level of timber houses using pre-post morphological processing combined with semantic segmentation by deep learning. From a simplified perspective, Kishiki et al [12] attempted to visualize the residual strength of buckled steel members.…”

Structural Damage Detection Technique of Secondary Building Components using Piezoelectric Sensor

“…The recent development of image sensing has realized damage detection using digital images. Earlier achievements by Chida and Takahashi [4] enabled the detection and evaluation of quantitative damage at the ground level of timber houses using pre-post morphological processing combined with semantic segmentation by deep learning. From a simplified perspective, Kishiki et al [12] attempted to visualize the residual strength of buckled steel members.…”