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Pipeline weld defect detection method and related device

A defect detection and defect technology, which is applied in the field of image processing, can solve problems such as poor model generalization performance, cumbersome training process, and poor detection effect, and achieve the effect of reducing the amount of model training, reducing sample data, and improving efficiency

Pending Publication Date: 2021-04-30
PING AN TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Due to the use of conventional target detection methods based on deep convolutional networks, a large amount of sample data is required in the stage of training the model, and the training process is cumbersome.
Moreover, for a specific pipeline type, there are few samples available for training the model, so the trained model has a poor detection effect for a specific pipeline type, and the model generalization performance is poor

Method used

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  • Pipeline weld defect detection method and related device
  • Pipeline weld defect detection method and related device
  • Pipeline weld defect detection method and related device

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Embodiment Construction

[0026] The pipeline weld seam defect detection method and related devices provided in the embodiments of the present application can improve the efficiency and detection effect of pipeline weld seam defect detection.

[0027] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiment of the application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiment of the application. Obviously, the described embodiment is only It is an embodiment of a part of the application, but not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application.

[0028] The terms "first", "second", "third", "fourth" and the like in the description and claims of the present application and the abo...

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Abstract

The invention relates to the field of image processing, and provides a pipeline weld defect detection method and a related device, and the method comprises the steps: obtaining an auxiliary picture set and a detection picture set; inputting the auxiliary picture set and the detection picture set into a feature extraction network to obtain a first feature map corresponding to the auxiliary picture set and a second feature map corresponding to the detection picture set; processing the first feature map and the second feature map by using a regional suggestion network to obtain a defect part predicted in the detection picture set; determining the similarity between the defect part marked in the auxiliary picture set and the defect part predicted in the detection picture set; and outputting a detection result according to the similarity. According to the technical scheme of the embodiment of the invention, the pipeline weld defect detection efficiency and detection effect can be improved.

Description

technical field [0001] The present application relates to the field of image processing, and in particular to a method for detecting defects of pipeline weld seams and related devices. Background technique [0002] The radiograph of the pipeline weld is an important document for the quality inspection of the pipeline weld. The radiograph of the pipeline weld is obtained through the ray detection method. Through the collection of the effective data of the radiograph, the operation status of the pipeline can be known in time, and leakage and fracture can be avoided. ACCIDENT. [0003] At present, when using the ray detection method to detect pipeline weld defects, the usual method is: use the conventional target detection method based on deep convolutional network for the ray pictures of pipeline welds to detect defects. Due to the use of conventional target detection methods based on deep convolutional networks, a large amount of sample data is required in the stage of train...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06K9/46G06K9/62G06N3/04G06N3/08G01N23/04
CPCG06T7/0002G06N3/08G01N23/04G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30168G01N2223/03G01N2223/628G06V10/44G06N3/045G06F18/22G06F18/214Y02P90/30
Inventor 卢春曦王健宗黄章成
Owner PING AN TECH (SHENZHEN) CO LTD
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