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@InProceedings{JerripothulaAnsaNijh:2021:ViSoTr,
               author = "Jerripothula, Koteswar Rao and Ansari, Sharik Ali and Nijhawan, 
                         Rahul",
          affiliation = "{Indraprastha Institute of Information Technology Delhi 
                         (IIIT-Delhi)} and {College of Engineering Roorkee (COER)} and 
                         {University of Petroleum and Energy Studies (UPES)}",
                title = "A Vision-based Solution for Track Misalignment Detection",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "railway, transfer learning, VGG, Inception.",
             abstract = "Derailment is one of the most frequent ways railway accidents 
                         happen. Track defects such as buckling and hogging that cause 
                         misalignment of tracks can easily lead to derailments. While 
                         railway tracks get laterally misaligned due to buckling, vertical 
                         misalignments can result from hogging. Such misalignments are 
                         visibly recognizable, and we can even automate recognition using 
                         data-driven models. This paper discusses how we build such 
                         data-driven models. There are no public datasets available to 
                         build such models; therefore, we introduce TMD (Track Misalignment 
                         Detection) dataset. It consists of misaligned and normal track 
                         images. The problem we try to solve here is essentially a binary 
                         image classification problem, which we solve by exploring the 
                         feature extraction approach to transfer learning (TL). In this 
                         approach, we employ a pre-trained network to extract rich 
                         features, which are then supplied with annotations to a learning 
                         algorithm for building a candidate TL model. Several pre-trained 
                         networks and learning algorithms exist, resulting in multiple 
                         candidate TL models; therefore, it becomes essential to identify 
                         effective ones. We propose an evaluation criterion to decide which 
                         are effective ones using our proposed bias-variance analysis. Our 
                         experiments demonstrate that the candidate TL models selected 
                         based on our proposed evaluation criterion perform better than 
                         other candidate TL models while testing.",
  conference-location = "Gramado (Virtual), Brazil",
      conference-year = "October 18th to October 22nd, 2021",
             language = "en",
           targetfile = "SIBGRAPI_Railway (3).pdf",
        urlaccessdate = "2022, Jan. 24"
}


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