@Article{icces.2023.09930,
AUTHOR = {Shan Xu, Huadu Tang, Ding Wang, Ruiguang Zhu, Liwei Wang, Shengwang Hao},
TITLE = {Damage Evaluation of Building Surface via Novel Deep Learning  Framework},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {27},
YEAR = {2023},
NUMBER = {4},
PAGES = {1--3},
URL = {http://www.techscience.com/icces/v27n4/55213},
ISSN = {1933-2815},
ABSTRACT = {Damage evaluation is an important index for the evaluation of buildings health. To provide a rapid crack
evaluation in practical applications, a crack identification and damage evaluation via deep learning 
framework is proposed in this paper. We built a combined dataset from Kaggle and site photos. A pre-trained 
U-net model is used to perform the training of model. With updated weights, the identification of cracks 
could be performed on non-labelled photos.},
DOI = {10.32604/icces.2023.09930}
}