High-speed rail overhead line system stay wire defect detection method based on deep learning

Abstract

The invention discloses a high-speed rail overhead line system stay wire defect detection method based on deep learning, and the method comprises the steps: carrying out the classification of defectsof a stay wire, and carrying out the labeling of a data sample containing a to-be-detected target according to the defect types of the stay wire; preprocessing the labeled data sample and converting the format of the labeled data sample; inputting the data sample of which the format is converted into a built network model, and outputting a prediction result; and carrying out post-processing on theprediction result to obtain a final defect detection result. By modeling the inherent attributes of the artificial object, the robustness of the network to the stay wire angle is improved, and the accuracy of traditional positive frame target detection is also improved. Meanwhile, according to the method provided by the invention, a complicated process of manually designing features is abandoned;defect features of key components are extracted by directly utilizing strong feature learning capability of a deep network, end-to-end defect detection is realized, a process of manually screening defect images is replaced, the workload of people is reduced, and a process from defect discovery to maintenance is shortened.

Description

technical field [0001] The invention belongs to the field of target detection, and more specifically relates to a deep learning-based detection method for cable defects of high-speed railway catenary. Background technique [0002] The catenary is an important part of the electrified railway, and its main function is to provide traction power to the electric locomotive. The catenary support and suspension device is responsible for the important task of supporting the catenary, and its status is closely related to the safety status of the catenary. Among them, the catenary suspension status monitoring system is to detect the technical status of the catenary suspension and its components. In particular, the guy wire is equivalent to the support of the entire catenary, and is the transmitter of vibration and force between the catenary cable and the catenary wire. For the defect detection of pull wire, there are few researches at home and abroad, and its defect identification is...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the defects of the prior art, the object of the present invention is to provide a method for detecting defects of high-speed rail catenary cables based on deep learning, aiming to use deep learning to solve the problem of improving the accuracy of defect detection of high-speed rail catenary with rotating cables

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