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The Automatic Rail Surface Multi-Flaw Identification Based on a Deep Learning Powered Framework
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AI
DOI：10.1109/TITS.2021.3109949.png)
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摘要
En 中文
Rails of unhealthy conditions are considered as major targets in the rail surface inspection and this study focuses on inspecting five types of major rail surface flaws, corrugations, defects, the shelling, squats, and grinding marks, via analyzing railway images. We propose a deep learning powered rail surface multi-flaw identification framework composed of two main components, a novel rail extractor for extracting rails from the background and a cascading rail surface flaw identifier for precisely identifying different flaws. The novelty of the cascading rail surface flaw identifier includes: 1) An unhealthy rail detector developed based on a DenseNet backbone for recognizing the healthy/unhealthy status on rail surfaces and 2) A rail flaw classifier for identifying flaw types on unhealthy rail surfaces. A new feature joint learning process integrating latent features derived from selected hierarchies of the DenseNet backbone as well as two traditional feature extractors, the local binary pattern and the gray level co-occurrence matrix, is developed to facilitate the rail flaw classifier to offer accurate identification results. The effectiveness of the proposed framework for rail surface multi-flaw identification is validated with datasets provided by an industrial partner in China and collected from online sources. Based on collected datasets, the proposed framework is capable to identify the rail with unhealthy conditions and its flaw type. The overall identification performance can achieve a 98.2% accuracy. Three groups of benchmarking methods are employed to verify advantages of the proposed framework. Computational results demonstrate the impressive performance of the proposed framework in the rail surface multi-flaw identification and its applicability on new datasets.
Keyword:
Rails
Corrugated surfaces
Surface morphology
Inspection
Rail transportation
Feature extraction
Surface treatment
Rail transportation
fault detection
deep neural network
data mining
condition monitoring
期刊
IF:
8.4
论文数:
9.7K
被引数:
6.3W
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