Method for creating digital radiographic inspection model for weld defects

A digital ray, detection model technology, applied in image data processing, instrumentation, calculation and other directions, can solve the problems of poor image effect, can not meet the requirements of high recall rate and so on

Pending Publication Date: 2020-10-02
西安数合信息科技有限公司
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Problems solved by technology

[0003] The purpose of the embodiments of the present invention is to provide a method for creating a digital ray detection model of weld defects, so as to solve the problem that the neural network model used in the public data set in the prior art is not effective for such images with inconspicuous features, and Problems that cannot meet the requirements of defect detection for high recall rate

Method used

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  • Method for creating digital radiographic inspection model for weld defects
  • Method for creating digital radiographic inspection model for weld defects
  • Method for creating digital radiographic inspection model for weld defects

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Embodiment 1

[0055] See figure 1 , an embodiment of the present invention provides a method for creating a digital ray detection model of weld defects, including:

[0056]S110. Obtain the original picture; the above original picture can be obtained directly from the database. Specifically, in this application, the above original picture is the original picture with welds. Specifically, whether the above welds are defective can be used as the above original picture of this solution .

[0057] S120. Using a predetermined method, extract the weld seam in the original image to obtain a weld seam image.

[0058] S130. Use a data enhancement algorithm to expand the sample of the weld image to obtain multiple sample images based on the weld image; use a data enhancement algorithm to expand the existing positive and negative samples, thereby increasing the capacity of the sample . According to the characteristics of the weld image, the original weld image is expanded by using image flip symmetr...

Embodiment 2

[0071] On the basis of the above-mentioned embodiment 1, this embodiment combines Figure 2-Figure 15 , to further describe the scheme in detail. details as follows:

[0072] Further, the predetermined method is:

[0073] Obtaining the region of interest of the original picture, and outlining the region of interest;

[0074] The image of the weld seam is extracted from the region of interest by using an edge detection method.

[0075] Specifically, the first step is to extract the region of interest from the image, that is, the weld.

[0076] Very important in image processing is ROI (region of interest), the region of interest. In machine vision and image processing, the area to be processed is outlined from the processed image in the form of a box, circle, ellipse, irregular polygon, etc., which is called the region of interest, ROI. Various operators (Operator operation symbols) and functions are commonly used in machine vision software such as Halcon, OpenCV, and Matl...

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Abstract

The invention provides a method for creating a digital radiographic inspection model for weld defects. A weld joint in an image is extracted by using a digital image processing technology; the numberof samples of the weld joint image is expanded by applying an image enhancement technology; the built deep learning model and the designed loss function are used for training the model, defect rules can be learned from existing defect images, all to-be-detected images can be processed in batches more flexibly by using the deep neural network, the method is more flexible, the performance is more stable, and the high recall rate and certain accuracy of the model are guaranteed; and the model is packaged and stored, so that the model is convenient to call and use. Manual workload is reduced, andworking efficiency is improved.

Description

technical field [0001] The invention relates to the technical field of image detection model creation methods, in particular to a digital ray detection model creation method for weld defects. Background technique [0002] In recent years, artificial intelligence has brought new developments to the field of computer vision, especially in image classification and object detection. Commonly used image classification models such as Alexnet, VGG, Resnet, etc. can handle extremely complex image data and are often used for classification of public datasets. The performance and accuracy of these models have been continuously improved. However, the data in actual production is not as easy to distinguish as common public data sets. For example, in the field of weld inspection, there is a problem of difficult film evaluation. There are certain defects in the welded joints, such as cracks, pores, slag inclusions, lack of penetration, lack of fusion, etc. The existence of these defects ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T7/0004G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/20032G06T2207/20132G06T2207/30152
Inventor 郑玲
Owner 西安数合信息科技有限公司
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