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Domain adversarial training for classification of cracking in images of concrete surfaces

AI in Civil Engineering 2022年第1期1卷 119-132页

作者： Bruno Oliveira Santos Jónatas Valença João P.Costeira Eduardo Julio CERIS Instituto Superior TécnicoUniversity of LisbonLisbonPortugal ISR Instituto Superior TécnicoUniversity of LisbonLisbonPortugal

The development of automatic methods to recognize cracks in surfaces of concrete has been under focus in recent years,firstly through computer vision methods and more recently focusing on convolutional neural networks that are delivering promising *** are still persisting in crack recognition,namely due to the confusion added by the myriad of elements commonly found on concrete *** robustness of these methods would deal with these elements if access to correspondingly heterogeneous datasets was *** so,this would be a cumbersome methodology,since training would be needed for each particular case and models would be case ***,efforts from the scientific community are focusing on generalizing neural network models to achieve high per-formance in images from different domains,slightly different from those in which they were effectively *** generalization of networks can be achieved by domain adaptation techniques at the training *** adapta-tion enables finding a feature space in which features from both domains are invariant,and thus,classes become *** work presented here proposes the DA-Crack method,which is a domain adversarial training method,to generalize a neural network for recognizing cracks in images of concrete *** domain adversarial method uses a convolutional extractor followed by a classifier and a discriminator,and relies on two datasets:a source labeled dataset and a target unlabeled small *** classifier is responsible for the classification of images randomly chosen,while the discriminator is dedicated to uncovering to which dataset each image *** from the discriminator reverses the gradient used to update the *** enables fighting the convergence promoted by the updating backpropagated from the classifier,and thus generalizing the extractor enabling it for crack recognition of images from both source and target *** show that

关键词： DA-Crack method Domain-adaptation Adversarial training network Crack detection concrete surfaces Computer vision

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