THE OPTIMIZATION OF RAILWAY FASTENER DEFECT DETECTION VIA ACTIVATION FUNCTION ADAPTATIONS

Manual control of rail defect detection is slow and costly. Deep learning methods can detect some of these defects to a certain extent. However, existing systems produce too many false positives due to environmental factors, resulting in labor and cost losses. One of the most important components in railway systems is the fastener, and their failure can lead to severe accidents. In this study, we developed a deep learning-based method that is designed to remain robust against foreign objects and environmental conditions when detecting railway fasteners. By employing various activation functions and expanding the training dataset through data augmentation techniques, our method significantly reduces false alarms. The best-performing activation function in our tests achieved an F1-score of 0.99 and a mean average precision (mAP) of 100%. Testing on a dataset provided by TCDD Railway Research & Technology Centre (DATEM) confirms the efficacy of our approach, demonstrating a notable decrease in unnecessary work and associated costs.