@Article{cmc.2020.06988,
AUTHOR = {Hongrui Zhang, Wenxue Wei, Xinguang Xiao, Song Yang, Wanlu Shao},
TITLE = {Method to Appraise Dangerous Class of Building Masonry Component Based on DC-YOLO Model},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {1},
PAGES = {457--468},
URL = {http://www.techscience.com/cmc/v63n1/38459},
ISSN = {1546-2226},
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 F<sub>1</sub> value, which indicates the good performance in classifying dangerous classes of building masonry component.},
DOI = {10.32604/cmc.2020.06988}
}