Intelligent Rail Flaw Detection System Based on Deep Learning and Support Vector Machine
Currently, detection systems of rail flaw detection vehicles in China have automatic flaw recognition function, which has problems with low accuracy, high false alarm rate and occurrence of underreport because of the adoption of simple logic judgment method based on the existing rules. In view of pr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2021-03-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128x.2021.02.016 |
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| Summary: | Currently, detection systems of rail flaw detection vehicles in China have automatic flaw recognition function, which has problems with low accuracy, high false alarm rate and occurrence of underreport because of the adoption of simple logic judgment method based on the existing rules. In view of problems proposed above, according to the characteristics of ultrasonic testing data, an intelligent rail flaw recognition system based on deep learning and support vector machine was proposed in this paper. Depth separable convolution and selective search was adopted to locate the target, and support vector machine method based on manually constructed multidimensional features was used to classify the flaws image in the system. The effectiveness of this measure was verified through the test of manually marked samples from actual running line data. The result showed that the intelligent rail flaw recognition system had excellent performance in many technical indicators of which the flaw detection was 99.8%, the false alarm rate reduced to 12% and the accuracy of classification was over 95%. |
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| ISSN: | 1000-128X |