X-ray weld defect detection method based on convolutional neural network

Abstract

The invention relates to an X-ray weld defect detection method based on a convolutional neural network, and the method comprises the following steps that a weld image data set containing different types of weld defects is built, and all weld images in the data set are marked with weld labels; an AF-RCNN model is established, wherein the AF-RCNN model comprises a backbone network module, a region generation module and a target classification and position regression module; the backbone network module adopts a residual network (ResNet) and a feature pyramid network (FPN) structure, an efficient convolution attention module is introduced between the residual network (ResNet) and the feature pyramid network (FPN) to enhance the learning ability of the network for unobvious defects and small target features, and a CIOU loss function is introduced to enhance the positioning ability of aiming frames; and an AF-RCNN model is trained by using the established data set, wherein the AF-RCNN model is used for classifying and positioning weld defects. The accuracy of all defects reaches 94% or above, and the detection speed is 11.65 FPS.

Description

technical field [0001] The invention belongs to the field of welding defect detection, and in particular relates to a method for detecting weld defects based on a convolutional neural network. Background technique [0002] Welded structures have been widely used in many fields, such as construction, vehicle, aerospace, railway, petrochemical and mechanical electrical. Due to the difference in environmental conditions and welding techniques, welding defects are inevitable during the welding process, so it is very important to check the quality of welds to ensure the reliability and safety of the structure. X-ray weld defect detection is one of the most commonly used non-destructive testing methods to detect welding quality. Researchers have done a lot of research on automatic detection of X-ray weld defects, and have achieved many important results. [0003] In the field of weld defect detection, traditional detection methods require inspectors to have sufficient experience ...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the characteristics of small target area of ​​weld defects and low contrast between defects and background, the present invention proposes a method for detecting weld defects based on convolutional neural network

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