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Contact net part defect detection method and device

A technology for defect detection and parts, applied in computer parts, neural learning methods, biological neural network models, etc., can solve the problem that the effectiveness of traditional methods depends on the quality of data annotation, inaccurate detection results, time-consuming and labor-intensive large-scale images, etc. problem, to achieve the effect of reducing model experience risk and improving accuracy

Pending Publication Date: 2022-06-03
SHENHUA BAOSHEN RAILWAY GRP
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Problems solved by technology

[0003] However, manual labeling of large-scale images is time-consuming and laborious, and the effectiveness of traditional methods is highly dependent on the quality of data labeling, resulting in inaccurate detection results.

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  • Contact net part defect detection method and device
  • Contact net part defect detection method and device
  • Contact net part defect detection method and device

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Embodiment Construction

[0058] In order to facilitate understanding of the present application, the present application will be described more fully below with reference to the related drawings. Embodiments of the present application are presented in the accompanying drawings. However, the application may be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used herein in the specification of the present application are for the purpose of describing specific embodiments only and are not intended to limit the present application.

[0060] Tian Wang and Yang Chen et al. proposed in the paper "A fast and robust convolutional neuralnetwork-based defect de...

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Abstract

The invention relates to an overhead line system part defect detection method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring image data of the catenary part; intercepting the image data to obtain a target template image; processing the image data based on a deep convolutional neural network model to obtain a feature extraction model; the deep convolutional neural network model comprises a convolutional self-encoding model; and according to the feature extraction model, performing similarity matching on the target template image and the to-be-detected part image so as to output a detection result. By adopting the method, the accuracy of the defect detection result of the catenary part can be effectively improved.

Description

technical field [0001] The present application relates to the technical field of image detection and processing, and in particular, to a method and device for defect detection of catenary parts. Background technique [0002] In the railway system, the detection and monitoring device (also known as the 4C system) of the catenary suspension state detection and monitoring device for nuts, cotter pins, insulators and other related parts needs to complete automatic identification and analysis in high-resolution images, so as to form maintenance suggestions and guidance Overhaul of the catenary. At present, almost all defect recognition relies on manual annotation data sets, and then uses the deep learning method of neural network to locate the target region (Region Of Interest, ROI), detect parts and identify defects. [0003] However, manual annotation of large-scale images is time-consuming and labor-intensive, and the effectiveness of traditional methods is highly dependent o...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06V10/44G06V10/74G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06T7/0004G06N3/088G06T2207/30164G06N3/045G06F18/22Y02P90/30
Inventor 张剑郭尽朝罗建涛
Owner SHENHUA BAOSHEN RAILWAY GRP
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