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http://dx.doi.org/10.6109/jkiice.2020.24.3.341

CNN-based Building Recognition Method Robust to Image Noises  

Lee, Hyo-Chan (Oceanic IT Convergence Technology Research Center, Hoseo University)
Park, In-hag (System Centroid Inc.)
Im, Tae-ho (Department of Information and Communication Engineering, Hoseo University)
Moon, Dai-Tchul (Oceanic IT Convergence Technology Research Center, Hoseo University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.3, 2020 , pp. 341-348 More about this Journal
Abstract
The ability to extract useful information from an image, such as the human eye, is an interface technology essential for AI computer implementation. The building recognition technology has a lower recognition rate than other image recognition technologies due to the various building shapes, the ambient noise images according to the season, and the distortion by angle and distance. The computer vision based building recognition algorithms presented so far has limitations in discernment and expandability due to manual definition of building characteristics. This paper introduces the deep learning CNN (Convolutional Neural Network) model, and proposes new method to improve the recognition rate even by changes of building images caused by season, illumination, angle and perspective. This paper introduces the partial images that characterize the building, such as windows or wall images, and executes the training with whole building images. Experimental results show that the building recognition rate is improved by about 14% compared to the general CNN model.
Keywords
Deep Learning; CNN; Building Recognition; Training Data Sets;
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