A Method of Data Augmentation for Classifying Road Damage Considering Influence on Classification Accuracy

This paper proposes a method for augmenting learning data of road damage dataset considering the influence of the augmented data on classification accuracy. Data augmentation is a very important task in the field of machine learning because more learning data causes increasing the accuracy of classification accuracy in general. The quality of the augmented data influences the accuracy of the classification. Effective data augmentation method for increasing classification accuracy is needed. The proposed method generates learning data by selecting effective data augmentation methods depending on the class of road damage. The method uses You Only Look Once v3 (YOLOv3) for detection and classification of road damage in an image. It is tuned by data adding the data augmented by the proposed method to the road damage dataset presented to the public. The experimental results show that the proposed method can increase the accuracy efficiently and effectively. The proposed selection of data augmentation methods improves remarkably mean Average Precision (mAP) which is one of the accuracy indices. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of KES International.