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Journals / Demiryolu Mühendisliği / 2026 / Sayı: 25

Label Scarcity in Rail Surface Defect Detection: Comparison of Unsupervised Anomaly Detection and Supervised Deep Learning Approaches under a Common Protocol

Ray Yüzeyi Kusurlarının Tespitinde Etiket Kıtlığı: Denetimsiz Anomali Tespiti ve Denetimli Derin Öğrenme Yaklaşımlarının Ortak Protokol Altında Karşılaştırılması

Ahmet Akkaya(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ)0000-0003-4836-2310
Pages
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Publication Date
2026-09-03
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results suggest that unsupervised anomaly detection can be a promising alternative under limited labeling budgets, with broader generalization to be tested on more diverse datasets.

📌 3 Key Takeaways

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Objective & Hypothesis:Although deep learning methods achieve high performance in image-based inspection of railway tracks, most of them require a sufficient number of labeled defect samples; in field conditions, however, defective samples are rare and labeling is costly.
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Key Findings & Data:In the unsupervised arm, PaDiM, PatchCore, FastFlow and EfficientAD are trained using only healthy images; in the supervised arm, ResNet50, MobileNetV3 and EfficientNet-B0 are trained with 100%, 25% and 10% of the available defect labels.
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Conclusion & Impact:The results suggest that unsupervised anomaly detection can be a promising alternative under limited labeling budgets, with broader generalization to be tested on more diverse datasets.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Ray kusur tespiti · Denetimsiz anomali tespiti · Derin öğrenme

Abstract

Although deep learning methods achieve high performance in image-based inspection of railway tracks, most of them require a sufficient number of labeled defect samples; in field conditions, however, defective samples are rare and labeling is costly. In this study, unsupervised anomaly detection and supervised classification approaches for detecting rail surface and fastener defects are compared under a common protocol in which both families are evaluated on exactly the same test set. In the unsupervised arm, PaDiM, PatchCore, FastFlow and EfficientAD are trained using only healthy images; in the supervised arm, ResNet50, MobileNetV3 and EfficientNet-B0 are trained with 100%, 25% and 10% of the available defect labels. All experiments are repeated with three random seeds, and performance is reported in terms of AUROC, PR-AUC, F1 and MCC together with parameter counts and inference times. The findings show that unsupervised methods reach an AUROC of 0.79 with zero defect labels, and that the superiority of supervised models emerges only at and above the 25% label level. In addition, a prevalence analysis in which the defect ratio of the test set is systematically varied quantifies the misleading nature of the accuracy metric under imbalanced conditions. The results suggest that unsupervised anomaly detection can be a promising alternative under limited labeling budgets, with broader generalization to be tested on more diverse datasets.

Özet

Demiryolu hatlarının görüntü tabanlı muayenesinde derin öğrenme yöntemleri yüksek başarım sağlamakla birlikte, bu yöntemlerin büyük bölümü yeterli sayıda etiketli kusur örneği gerektirmektedir; oysa saha koşullarında kusurlu örnekler nadirdir ve etiketleme maliyetlidir. Bu çalışmada, ray yüzeyi ve bağlantı elemanı kusurlarının tespitinde denetimsiz anomali tespiti ile denetimli sınıflandırma yaklaşımları, birebir aynı test kümesinde ölçüldükleri ortak bir protokol altında karşılaştırılmıştır. Denetimsiz kolda PaDiM, PatchCore, FastFlow ve EfficientAD yöntemleri yalnızca sağlam görüntülerle; denetimli kolda ResNet50, MobileNetV3 ve EfficientNet-B0 mimarileri kusur etiketlerinin %100'ü, %25'i ve %10'u ile eğitilmiştir. Tüm deneyler üç farklı rastgele tohumla tekrarlanmış; başarım AUROC, PR-AUC, F1 ve MCC metrikleriyle birlikte parametre sayısı ve çıkarım süresi üzerinden raporlanmıştır. Bulgular, denetimsiz yöntemlerin sıfır kusur etiketiyle 0,79 AUROC düzeyine ulaştığını ve denetimsiz PatchCore'un %10 etiketli denetimli modellerle denk kaldığını, denetimli üstünlüğün ancak %25 etiket düzeyi ve üzerinde belirginleştiğini göstermektedir. Ayrıca test kümesindeki kusur oranının sistematik olarak değiştirildiği prevalans analiziyle, doğruluk metriğinin dengesiz koşullardaki yanıltıcılığı nicel olarak gösterilmiştir. Sonuçlar, etiket bütçesinin kısıtlı olduğu koşullarda denetimsiz anomali tespitinin umut verici bir alternatif olabileceğine işaret etmektedir.

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Cite This Article

Akkaya, A. (2026). Label Scarcity in Rail Surface Defect Detection: Comparison of Unsupervised Anomaly Detection and Supervised Deep Learning Approaches under a Common Protocol. Demiryolu Mühendisliği, Sayı: 25. https://doi.org/10.47072/demiryolu.2002891

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