Mask R-CNN Derin Sinir Ağı ile Demiryolu Hattı Bileşenlerinde Çoklu Hata Tespiti

Demiryolu birçok yolcunun aynı anda seyahat edebilmesine olanak tanıyan aynı zamanda yük taşımacılığında da sıklıkla kullanılan bir ulaşım çeşididir. Ulaşımda kazalar ve aksamalar meydana gelmemesi için hattın periyodik olarak kontrolünün sağlanması ve hatalı bileşenlerin belirlenerek onarılması gerekmektedir. Raylı ulaşım sistemlerinin güvenliğinin sağlanması için yapılan manuel denetimlere alternatif olarak, son zamanlarda görüntü işleme algoritmaları ve derin öğrenme algoritmaları kullanılarak temassız, hızlı ve güvenilir sonuçlar veren hata tespit yöntemleri geliştirilmiştir. Bu çalışmada sağlıklı olan traversler ve travers üzerinde meydana gelen çeşitli hataların tespit edilmesine yönelik Mask R-CNN derin sinir ağı mimarisi kullanılarak yeni bir yöntem önerildi. Üç farklı hata türü ve sağlıklı travers olmak üzere toplamda dört farklı sınıf etiketi ile etiketlenen gerçek demiryolu görüntüleri kullanılarak model eğitimi ve eğitilen modelin test edilmesi sağlandı. Değerlendirme metrikleri hesaplanarak modelin başarı performansı ölçüldü. Sağlıklı ve hatalı olan traversleri belirlemede modelin doğruluğu %95 olarak belirlendi.

Multiple Fault Detection in Railway Components with Mask R-CNN Deep Neural Network

Railway is a type of transportation that allows many passengers to travel at the same time and is often used in freight transportation. In order to prevent accidents and disruptions in transportation, the line is checked periodically, faulty components are determined and repaired or replaced with new ones. As an alternative to manual inspections to ensure the safety of rail transportation systems, defect detection methods that provide contactless, fast and reliable results have been developed recently by using image processing algorithms and deep learning algorithms. In this study, a new method is proposed using Mask R-CNN deep neural network architecture to detect healthy sleepers and various faults on the sleeper. Model training and testing of the trained model were provided by using real railway images labeled with four different class labels, three different error types and healthy sleeper. The success performance of the model was measured by calculating the evaluation metrics. The accuracy of the model was determined as 95% in determining the healthy and faulty sleepers

___

  • ⦁ Tastimur, C., Yaman, O., Karakose, M., Akin, E. 2017. A Real Time Interface for Vision Inspection of Rail Components and Surface in Railways. In 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), IEEE, 1-6.
  • ⦁ Li, Y., Trinh, H., Haas, N., Otto, C., Pankanti, S., 2013. Rail Component Detection, Optimization, and Assessment for Automatic Rail Track Inspection. IEEE Transactions on Intelligent Transportation Systems, 15(2), 760-770.
  • ⦁ Taştimur, C., 2017. Demiryolu Raylarında Makas Geçişlerinin Görüntü İşleme Tabanlı Temassız İzleme Yöntemiyle Tespit Edilmesi. Yüksek Lisans Tezi, Fırat Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Anabilim Dalı, Elazığ, 132.
  • ⦁ Guo, F., Qian, Y., Rizos, D., Suo, Z., Chen, X. 2021. Automatic Rail Surface Defects İnspection Based on Mask R-CNN. Transportation Research Record, 2675(11), 655-668.
  • ⦁ Chandran, P., Asber, J., Thiery, F., Odelius, J., Rantatalo, M. 2021. An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning. Sustainability, 13(21), 12051.
  • ⦁ Yilmazer, M., Karakose, M., Aydin, I. 2021, October. Detection and Measurement of Railway Expansion Gap with Image Processing. In 2021 International Conference on Data Analytics for Business and Industry (ICDABI), IEEE, 515-519.
  • ⦁ Yilmazer, M., Karakose, M., Aydin, I., 2021. Determination of Railway Track Gauge with Image Processing. In 2021 International Conference on Data Analytics for Business and Industry (ICDABI), IEEE, 510-514.
  • ⦁ Franca, A.S., Vassallo, R.F. 2020. A Method of Classifying Railway Sleepers and Surface Defects in Real Environment. IEEE Sensors Journal, 21(10), 11301-11309.
  • ⦁ Liu, J., Teng, Y., Shi, B., Ni, X., Xiao, W., Wang, C., Liu, H. 2021. A Hierarchical Learning Approach for Railway Fastener Detection Using Imbalanced Samples. Measurement, 186, 110240.
  • ⦁ Faghih-Roohi, S., Hajizadeh, S., Núñez, A., Babuska, R., De Schutter, B. 2016. Deep Convolutional Neural Networks for Detection of Rail Surface Defects. In 2016 International joint conference on neural networks (IJCNN), IEEE, 2584-2589.
  • ⦁ Gibert, X., Patel, V.M., Chellappa, R., 2015. Robust Fastener Detection for Autonomous Visual Railway Track Inspection. In 2015 IEEE Winter Conference on Applications of Computer Vision, IEEE, 694-701.
  • ⦁ Chen, Z., Wang, Q., Yu, T., Zhang, M., Liu, Q., Yao, J., He, Q. 2022. Foreign Object Detection for Railway Ballastless Trackbeds: A Semisupervised Learning Method. Measurement, 110757.
  • ⦁ Singh, A.K., Dwivedi, A.K., Nahar, N., Singh, D., 2021. Railway Track Sleeper Detection in Low Altitude UAV Imagery Using Deep Convolutional Neural Network. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, IEEE, 355-358.
  • ⦁ Yanan, S., Hui, Z., Li, L., Hang, Z., 2018. Rail Surface Defect Detection Method Based on Yolov3 Deep Learning Networks. In 2018 Chinese Automation Congress (CAC), IEEE, 1563-1568.
  • ⦁ Guo, F., Qian, Y., Shi, Y. 2021. Real-Time Railroad Track Components Inspection Based on the Improved Yolov4 Framework. Automation in Construction, 125, 103596.
  • ⦁ Zhuang, L., Qi, H., Zhang, Z. 2021. The Automatic Rail Surface Multi-Flaw Identification Based on a Deep Learning Powered Framework. IEEE Transactions on Intelligent Transportation Systems.
  • ⦁ Zhou, Q., 2021. A Detection System for Rail Defects Based on Machine Vision. In Journal of Physics: Conference Series, IOP Publishing, 1748(2), 022012.
  • ⦁ Zheng, Z., Qi, H., Zhuang, L., Zhang, Z., 2021. Automated Rail Surface Crack Analytics Using Deep Data-Driven Models and Transfer Learning. Sustainable Cities and Society, 70, 102898.
  • ⦁ Marino, F., Distante, A., Mazzeo, P.L., Stella, E., 2007. A Real-Time Visual Inspection System for Railway Maintenance: Automatic Hexagonal- Headed Bolts Detection. IEEE Transactions on Systems, Man, and Cybernetics, Part C, Applications and Reviews, 37(3), 418-428.
  • ⦁ Feng, H., Jiang, Z., Xie, F., Yang, P., Shi, J., Chen, L., 2013. Automatic Fastener Classification and Defect Detection in Vision- Based Railway Inspection Systems. IEEE Transactions on Instrumentation and Measurement, 63(4), 877-888.
  • ⦁ Wu, Y., Qin, Y., Qian, Y., Guo, F., Wang, Z., Jia, L. 2022. Hybrid Deep Learning Architecture for Rail Surface Segmentation and Surface Defect Detection. Computer-Aided Civil and Infrastructure Engineering, 37(2), 227-244.
  • ⦁ Ye, W., Deng, S., Ren, J., Xu, X., Zhang, K., Du, W., 2022. Deep Learning-Based Fast Detection of Apparent Concrete Crack in Slab Tracks with Dilated Convolution. Construction and Building Materials, 329, 127157.
  • ⦁ He, K., Gkioxari, G., Dollár, P., Girshick, R., 2017. Mask r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, 2961-2969.
  • ⦁ Markoulidakis, I., Rallis, I., Georgoulas, I., Kopsiaftis, G., Doulamis, A., Doulamis, N., 2021. Multiclass Confusion Matrix Reduction Method and its Application on Net Promoter Score Classification Problem. Technologies, 9(4), 81.
  • ⦁ Bojarczak, P., Lesiak, P. 2021. Uavs in Rail Damage Image Diagnostics Supported by Deep- Learning Networks. Open Engineering, 11(1), 339-348.
Çukurova Üniversitesi Mühendislik Fakültesi dergisi-Cover
  • ISSN: 2757-9255
  • Yayın Aralığı: Yılda 4 Sayı
  • Başlangıç: 2009
  • Yayıncı: ÇUKUROVA ÜNİVERSİTESİ MÜHENDİSLİK FAKÜLTESİ