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Open AccessArticle

Automatic Object-Detection of School Building Elements in Visual Data: A Gray-Level Histogram Statistical Feature-Based Method

1
Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong 999077, China
2
Department of Civil Engineering and Engineering Mechanics, Columbia University, New York, NY 10027, USA
3
Department of Civil Engineering Institute of Construction Engineering and Management, National Central University, Taoyuan City 32001, Taiwan
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Research Center for Construction Leaking Accreditation, National Central University, Taoyuan City 32001, Taiwan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(18), 3915; https://doi.org/10.3390/app9183915
Received: 16 August 2019 / Revised: 15 September 2019 / Accepted: 16 September 2019 / Published: 18 September 2019
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. View Full-Text
Keywords: column and wall detection; imaging recognition; structural information collection; gray-level histogram column and wall detection; imaging recognition; structural information collection; gray-level histogram
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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.

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