An Investigation into Data Augmentation by Rotation for Rock Identification Using AI

Abstract

In recent years, AI image recognition technology has become increasingly practical. In the construction industry, there is a growing trend to utilize image recognition, such as rock identification using Convolutional Neural Networks (CNNs). When employing CNNs, a substantial amount of image data is required as training. In situations where obtaining sufficient data is challenging, data augmentation is used to increase the dataset size. This augmentation creates new images not present in the original set. However, a detailed examination of the impact of these augmented images on the accuracy of rock identification using CNNs has not been conducted. In this study, we created and evaluated three models based on images generated through data augmentation. As a result, we demonstrated an instance where the influence of newly generated images tends to bias the results of rock identification.