Railway Infrastructure Defects Recognition using Fine-grained Deep Convolutional Neural Networks

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Conference Proceeding
2018 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2018, 2019
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DICTA2018_UTS_Huaxi.Huang (1).pdfAccepted Manuscript1.05 MB
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© 2018 IEEE. Railway power supply infrastructure is one of the most important components of railway transportation. As the key step of railway maintenance system, power supply infrastructure defects recognition plays a vital role in the whole defects inspection sub-system. Traditional defects recognition task is performed manually, which is time-consuming and high-labor costing. Inspired by the great success of deep neural networks in dealing with different vision tasks, this paper presents an end-to-end deep network to solve the railway infrastructure defects detection problem. More importantly, this paper is the first work that adopts the idea of deep fine-grained classification to do railway defects detection. We propose a new bilinear deep network named Spatial Transformer And Bilinear Low-Rank (STABLR) model and apply it to railway infrastructure defects detection. The experimental results demonstrate that the proposed method outperforms both hand-craft features based machine learning methods and classic deep neural network methods.
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