Development of a pre-training method for improving the accuracy of railway equipment type classification based on train front-view images

Abstract

One effective approach for effective railway maintenance is the use of train front-view images combined with machine learning. However, this approach has so far been limited to components that can be clearly captured in front-view images. On the other hand, although joint bolts and rail bonds require significant maintenance resources, they appear only partially and unclearly in front-view images, making it difficult to detect them or determine their type and condition using deep learning. This study focuses on rail bonds, which exhibit significant differences in service life depending on their type. A machine learning model is pretrained using high-resolution in-situ measurement images, and then fine-tuned using front-view images obtained on train, with the aim of improving classification accuracy based on unclear front-view images. Verification on actual railway lines demonstrated that the proposed method improves classification accuracy by 25% compared to models trained using only front-view images.