Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/81698
Title: Automatic object-detection of school building elements in visual data : a gray-level histogram statistical feature-based method
Authors: Zhang, Z 
Wei, HH 
Yum, SG
Chen, JH
Keywords: Column and wall detection
Imaging recognition
Structural information collection
Gray-level histogram
Issue Date: 2019
Publisher: Molecular Diversity Preservation International (MDPI)
Source: Applied sciences, 18 Sept. 2019, v. 9, no. 18, 3915, p. 1-16 How to cite?
Journal: Applied sciences 
Abstract: Automatic object-detection technique can improve the efficiency of building data collection for semi-empirical methods to assess the seismic vulnerability of buildings at a regional scale. However, current structural element detection methods rely on color, texture and/or shape information of the object to be detected and are less flexible and reliable to detect columns or walls with unknown surface materials or deformed shapes in images. To overcome these limitations, this paper presents an innovative gray-level histogram (GLH) statistical feature-based object-detection method for automatically identifying structural elements, including columns and walls, in an image. This method starts with converting an RGB image (i.e. the image colors being a mix of red, green and blue light) into a grayscale image, followed by detecting vertical boundary lines using the Prewitt operator and the Hough transform. The detected lines divide the image into several sub-regions. Then, three GLH statistical parameters (variance, skewness, and kurtosis) of each sub-region are calculated. Finally, a column or a wall in a sub-region is recognized if these features of the sub-region satisfy the predefined criteria. This method was validated by testing the detection precision and recall for column and wall images. The results indicated the high accuracy of the proposed method in detecting structural elements with various surface treatments or deflected shapes. The proposed structural element detection method can be extended to detecting more structural characteristics and retrieving structural deficiencies from digital images in the future, promoting the automation in building data collection.
URI: http://hdl.handle.net/10397/81698
ISSN: 2076-3417
DOI: 10.3390/app9183915
Rights: © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Zhang, Z.; Wei, H.-H.; Yum, S.G.; Chen, J.-H. Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method. Appl. Sci. 2019, 9, 3915, 1-16 is available at https://dx.doi.org/10.3390/app9183915
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