Railway Infrastructure Defects Recognition using Fine-grained Deep Convolutional Neural Networks
| Autor: | Christina Kirsch, Qiang Wu, Jian Zhang, Huaxi Huang, Jingsong Xu |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Computer science Distributed computing Bilinear interpolation Railway transportation 02 engineering and technology 010501 environmental sciences 01 natural sciences Convolutional neural network Maintenance system law.invention law 0202 electrical engineering electronic engineering information engineering Deep neural networks 020201 artificial intelligence & image processing Transformer Activity-based costing 0105 earth and related environmental sciences |
| Zdroj: | DICTA |
| DOI: | 10.1109/dicta.2018.8615868 |
| Popis: | © 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. |
| Databáze: | OpenAIRE |
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