Work

The Optimization of Railway Fastener Defect Detection Via Activation Function Adaptations

Year: 2024

Type: article

Abstract: 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.

Source: Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering

DOI: 10.18038/estubtda.1479970

Authors: Rıdvan Özdemir, Mehmet Koç

Institution: Eskisehir Technical University

Language: English

Topic: Infrastructure Maintenance and Monitoring

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funder: Eskişehir Teknik Üniversitesi