Abstract
Abstract. The historical castles (castellated walls), which are cultural heritages in Japan, require regular maintenance, and it is necessary to record the arrangement of individual wall stones in the maintenance work. Recently, image processing techniques are practiced to optimize maintenance and management of the infrastructure assets. In the previous study, we proposed an automatic method for efficiently extracting individual wall stone polygons by improved multiscale image segmentation technique. However, the problem has remained that wall stone polygons could not be extracted properly when there were no clear gaps or boundaries between stones. To address this problem, we improved the multiscale image segmentation technique used in our previous studies. The first improvement is that in the region growing process, selecting the best combination of a plurality of objects instead of two. The second improvement is the modification of the shape criterion to be used. Besides, we proposed three-stage Stacked cGAN for wall stone edge detection that enables us to complement areas with weak or broken boundaries of stone edges. This approach is composed of a coarse-to-fine based image-to-edges translation network. The edge images derived from this method are used as the additional channel in multiscale image segmentation with a higher weight compared to the other RGB channels. It was confirmed that the separation performance of individual wall stone polygons was improved by the proposed method. Furthermore, the proposed method is highly effective to reduce the difficulty in setting of the scale parameter, which is usually sensitive to segmentation results and requires trial and error.
Highlights
There are hundreds of historical castles in Japan and some of which are designated as national treasures and important cultural properties
We proposed a method for efficiently extracting individual wall stone polygons by improved multiscale image segmentation technique (Sakamoto et al, 2018)
We focused on the fact that many wall stones have a convex hull shape, and we introduced the new shape criterion called convex hull fitness and showed that it is possible to improve the performance for the extraction of wall stone polygons
Summary
There are hundreds of historical castles (castellated walls) in Japan and some of which are designated as national treasures and important cultural properties. It is difficult to identify individual wall stones after dismantling by this method. Another approach is to attach integrated circuits (IC) tags for identifying the individual wall stones before dismantling the castellated walls for restoration (Ryu et al, 2014). It is quite burdensome to attach IC tags on each stone and to capture the images individually. As a study similar to our theme, there is one to detect bricks from masonry walls (Ibrahim et al, 2019). This approach combines U-Net based brick seed localization and the Watershed algorithm for accurate instance segmentation of bricks. The processing result almost depends on the extraction state of the seed region by U-Net
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