Automatic Detection of Cracks on Concrete Surfaces in the Presence of Shadows

被引:26
作者
Palevicius, Paulius [1 ]
Pal, Mayur [1 ]
Landauskas, Mantas [1 ]
Orinaite, Ugne [1 ]
Timofejeva, Inga [1 ]
Ragulskis, Minvydas [1 ]
机构
[1] Kaunas Univ Technol, Dept Math Modelling, Ctr Nonlinear Syst, LT-51368 Kaunas, Lithuania
关键词
concrete crack detection; deep learning; convolution neural networks; image classification; image augmentation;
D O I
10.3390/s22103662
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
Deep learning-based methods, especially convolutional neural networks, have been developed to automatically process the images of concrete surfaces for crack identification tasks. Although deep learning-based methods claim very high accuracy, they often ignore the complexity of the image collection process. Real-world images are often impacted by complex illumination conditions, shadows, the randomness of crack shapes and sizes, blemishes, and concrete spall. Published literature and available shadow databases are oriented towards images taken in laboratory conditions. In this paper, we explore the complexity of image classification for concrete crack detection in the presence of demanding illumination conditions. Challenges associated with the application of deep learning-based methods for detecting concrete cracks in the presence of shadows are elaborated on in this paper. Novel shadow augmentation techniques are developed to increase the accuracy of automatic detection of concrete cracks.
引用
收藏
页数:13
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