Crack Detection based on Convnext and Normalization

Abstract

Cracks, mostly caused by irregular expansion and contraction, indicating potential damage to a building, are of great significance to building quality assessment and predicting potential disasters like earthquakes. In this paper, after a comparison of the conventional methods using image processing techniques to detect the cracks and the newly-proposed CNN method, ConvNeXt is involved, which shows more sufficiency and stability. At the stage of the experiment, due to the specialization of the datasets and the methodology, a serial of image processing is engaged as the preprocessing before the data are used to train the CNN model. By using two crack-specialized datasets, namely, Concrete Crack Images for Classification and the SDNET2018, over 60,000 images are selected as the training samples. After adequate training and the AdamW involved as the optimizer, an accuracy of 99.0% on the dataset is reached and the expected results of accuracy of 99.0% are obtained.