Li, M, Peng, B, Liu, J orcid.org/0000-0002-5391-7213 et al. (1 more author) (2023) RBNet: An Ultra Fast Rendering-based Architecture for Railway Defects Segmentation. IEEE Transactions on Instrumentation and Measurement, 72. 2512808. ISSN 0018-9456
Abstract
Inspection of railway defects is crucial for the safe and efficient operation of trains. Recent advancements in convolutional neural networks have led to the development of many effective detection and segmentation algorithms, however, these algorithms often struggle to balance efficiency and precision. In this paper, we present a rendering-based fully convolutional network that generates segmentation results through a coarse-to-fine approach. This allows our framework to make full use of low-level features while minimizing the number of parameters. Additionally, our network generates segmentation results from multiple scales of the feature map and uses residual connections to improve low-level feature detection. To improve training, we propose a novel method that augments the dataset by cutting and pasting images and corresponding ground truth labels horizontally. To better understand the patterns learned by our model, we also generate importance and uncertainty maps to make our model explainable. Our results show that the proposed method outperforms other state-of-the-art image segmentation methods with a higher frame rate and better performance.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | railway surface defects, image segmentation, rendering mechanism |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 24 Apr 2023 15:00 |
Last Modified: | 20 Jul 2023 14:22 |
Published Version: | https://ieeexplore.ieee.org/document/10106288 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/tim.2023.3269107 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:198463 |