On the enhancement of semi-supervised deep learning-based railway defect detection using pseudo-labels

Abstract

In recent years, the use of deep learning methods in railway defect detection has expanded rapidly due to the technology’s ability to improve accuracy and efficiency in fault diagnosis. However, modeling deep learning applications requires a large amount of data, and the labeling process for such datasets demands significant manpower. To address this gap this paper introduces a semi-supervised learning approach using a student–teacher model with YOLOv4 for automatically labeling images, aiming to reduce the manual effort required for image classification. We also present a novel dataset generated from images provided by the TCDD (The State Railways of the Republic of Turkey), which includes five distinct railway defects. Results of the experiments on the same test dataset show that the proposed student–teacher model not only improves YOLOv4’s detection performance according to several decision metrics but also extends the training set with high-confidence pseudo-labeled images.