Open Access

ARTICLE

Method to Appraise Dangerous Class of Building Masonry Component Based on DC-YOLO Model

Hongrui Zhang¹, Wenxue Wei^{1, *}, Xinguang Xiao¹, Song Yang¹, Wanlu Shao¹

1 College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China.

* Corresponding Author: Wenxue Wei. Email: .

Computers, Materials & Continua 2020, 63(1), 457-468. https://doi.org/10.32604/cmc.2020.06988

Received 21 April 2019; Accepted 18 July 2019; Issue published 30 March 2020

Abstract

This DC-YOLO Model was designed in order to improve the efficiency for appraising dangerous class of buildings and avoid manual intervention, thereby making the appraisal results more objective. It is an automated method designed based on deep learning and target detection algorithms to appraise the dangerous class of building masonry component. Specifically, it (1) adopted K-means clustering to obtain the quantity and size of the prior boxes; (2) expanded the grid size to improve identification to small targets; (3) introduced in deformable convolution to adapt to the irregular shape of the masonry component cracks. The experimental results show that, comparing with the conventional method, the DC-YOLO model has better recognition rates for various targets to different extents, and achieves good effects in precision, recall rate and F₁ value, which indicates the good performance in classifying dangerous classes of building masonry component.

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Keywords

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