Defect detection method for industrial ray weld joint image

Abstract

The invention discloses a defect detection method for an industrial ray weld joint image. The method comprises a step of preprocessing a plurality of weld joint image data, then performing Fourier transform to obtain an amplitude spectrum thereof, and taking a first quadrant of the amplitude spectrum as image feature data, a step of marking the image feature data and dividing the image feature data into a training set matrix and a testing set matrix to be inputted into a classifier of a support vector machine, and training and testing the classifier to obtain the accuracy of the classifier inweld joint defect identification, a step of shooting an actual weld joint, preprocessing a weld joint image and performing Fourier transform to obtain an amplitude spectrum, and taking the first quadrant of the amplitude spectrum as image feature data, and a step of inputting the image feature data into the classifier, allowing the classifier to identify a weld joint defect, and judging that the weld joint defect exists if an identification probability is larger than or equal to a classifier weld joint defect identification probability, otherwise judging that the weld joint is normal. According to the method, the detection accuracy of the weld joint defect is improved, the detection cost is reduced, and the method has high adaptability and is suitable for most weld joint image detection and analysis.

Description

technical field [0001] The invention relates to a defect detection method for industrial ray welding seam images. Background technique [0002] In industrial production, due to various problems that may occur during the welding process, various defects such as pores and slag inclusions will be caused in the weld, which will affect the quality of the product. Therefore, the defect detection of the weld image is particularly important. Traditional industrial ray weld images are usually inspected manually for defect detection. The accuracy varies from person to person, depending on experience, and the manual workload is large, the labor cost is high, and there is a possibility of missing defects; therefore, the weld image The realization of automatic detection is of great significance to the standardization and standardization of production. At present, there are also some automatic identification and classification methods for industrial ray weld images. The welding seam def...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a defect detection method for industrial ray weld images, this method overcomes the traditional manual and automatic weld image recognition defects, effectively improve the accuracy of weld defect detection and reduce detection costs, and this method has strong adaptability and is suitable for the detection and analysis of most weld images

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