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A comparison of deep convolutional neural networksfor image-based detection of concrete surface cracks

Słoński Marek

Computer Assisted Methods in Engineering and Science

2019

Vol. 26, no. 2

105--112

The aim of this paper is to compare the performance of four deep convolutional neural networks in theproblem of image-based automated detection of concrete surface cracks in the case of a small dataset. Thiscrack detection problem is treated as a binary classification problem, and it is solved by training a deepconvolutional neural network on the small dataset. In this context, overfitting during training was the mainissue to cope with and various techniques were applied to overcome this issue. The results of the experi-ments suggest that the best approach for this problem is to use the pretrained convolutional base of a largepretrained convolutional neural network as an automatic feature extraction method and adding a new bi-nary classifier on top of the convolutional base. Then, at the training the new classifier and fine-tuningthe last few layers of the pretrained network take place at the same time. The classification accuracy of thebest deep convolutional neural network on the testing set is about 94%.