Investigation of a class imbalance handling method for mountain tunnel face evaluation AI using generative AI

Abstract

In recent years, the application of AI to mountain tunnel face evaluation has been expanding, with particular attention given to classification using face images. However, class imbalance in training data poses a challenge, resulting in reduced classification accuracy. In this study, to address class imbalance, we utilized generative AI to introduce weighted cross-entropy and trained an Artificial Neural Network (ANN). As a result, improvements in classification accuracy were observed for certain evaluation criteria compared to conventional methods, suggesting the effectiveness of data correction using generative AI. On the other hand, accuracy degradation was noted for specific evaluation items, indicating room for improvement. This study demonstrates the potential of generative AI as a method for addressing class imbalance and presents a new approach for in-house AI development.