• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.37 no.4
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• pp.219-231
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• 2019

The specifications of signboards are set for each type of signboards, but the shape and size of the signboard actually installed are not uniform. In addition, because the colors of the signboard are not defined, so various colors are applied to the signboard. Methods for recognizing signboards can be thought of as similar methods of recognizing road signs and license plates, but due to the nature of the signboards, there are limitations in that the signboards can not be recognized in a way similar to road signs and license plates. In this study, we proposed a methodology for recognizing plate-type signboards, which are the main targets of illegal and old signboards, and automatically extracting areas of signboards, using the deep learning-based Faster R-CNN algorithm. The process of recognizing flat type signboards through signboard images captured by using smartphone cameras is divided into two sequences. First, the type of signboard was recognized using deep learning to recognize flat type signboards in various types of signboard images, and the result showed an accuracy of about 71%. Next, when the boundary recognition algorithm for the signboards was applied to recognize the boundary area of the flat type signboard, the boundary of flat type signboard was recognized with an accuracy of 85%.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.1
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• pp.69-83
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• 2020

Signboards are standardized according to national legislation for the safety of pedestrians and disaster prevention in urban areas. Signboards should be installed according to the standard. However, it is not easy to manage the signboards systematically due to the number of signboards that have been installed for a long time and frequently changing stores. In this study, we proposed a methodology for distinguishing signboards that deviated from the standard. To this end, the signboard was photographed using a stereo camera, and then the three-dimensional coordinates of the signboard were determined from the signboard image to calculate the signboard's horizontal and vertical dimensions to determine the signboard's specifications. In order to determine the interior and relative orientation parameters of the stereo camera, an outdoor three-dimensional building was used as the test field. Then, the image coordinates of four vertices of the signboard were extracted from the signboard image taken from about 15m ~ 22m distance using deep learning. After determining the signboard's three-dimensional coordinates by using the interior and relative orientation parameters of the stereo camera and the image coordinates of the four vertices of the signboard, the horizontal and vertical sizes of the signboard were calculated, resulting in an error of about 2.7cm on average. The specifications for the ten flat-type signboards showed that all of the horizontal sizes were compliant with the specifications, but the vertical sizes exceeded about 36.5cm on average. Through this, it was found that maintenance of flat-type signboards is needed overall.