Verification of learning accuracy in crack segmentation using data cleansing based on image features

Abstract

In deep learning, data cleansing is effective in improving the accuracy of the model. On the other hand, the number of data is also an important factor for proper training. Therefore, when performing data cleansing, it is necessary to apply an effective method. Based on this problem, this study verified the effect of data cleansing on the crack segmentation for revetment. In the verification, various datasets was created based on the features of training images. And training results was compared for each dataset.