An efficient construction method of accurate crack detection model using SimCLR

Abstract

Recently, many studies have been conducted to develop techniques to detect cracks from concrete surface images using deep learning to improve the efficiency of crack evaluation in concrete structures. However, the annotation of a large amount of training data is required for the construction of a high-performance model, which is a labor-saving issue. In this paper, we propose to apply SimCLR to the training of CNN models to construct highly accurate models with a few annotations. In our experiments in which 5.6% of the training data is annotated with class labels, the results show that our model pre-trained using SimCLR performs better than models pre-trained using ImageNet or other datasets.