Title: A novel image-based scheme for automated detection and identification of weld defects

Authors: Zheng Wang; Lu Li; Weixin Gao

Addresses: Department of Electrical Engineering, Xi'an Shiyou University, Xi'an Dianzi er Lu, Shaanxi – 710065, China ' Department of Electrical Engineering, Xi'an Shiyou University, Xi'an Dianzi er Lu, Shaanxi – 710065, China ' Department of Electrical Engineering, Xi'an Shiyou University, Xi'an Dianzi er Lu, Shaanxi – 710065, China

Abstract: A guide line setting method is proposed to accurately extract the region of interest (ROI) of the weld. The slice image is segmented from ROI. After noise reduction of the weld slice images using the mean filtering, a contrast check algorithm is proposed to decide whether to enhance the slice images. Then, the suspected defect region (SDR) is obtained by vision-based density clustering segmentation. SDRs can be identified as defects or noise. In this recognition process, a sparse dictionary learning approach is used. After we get the dictionary, we need to solve the corresponding coefficients for each SDR to be tested. The coefficient is obtained by solving the problem of minimising the one-norm of the matrix. From the coefficients, the specific category information of SDR can be directly extracted. Experiments show that when the parameters are properly selected, the sensitivity and specificity of the algorithm are 98.2% and 98.0%. [Submitted 15 March 2022; Accepted 13 November 2023]

Keywords: weld non-destructive testing; image processing; machine vision; coefficient dictionary.

DOI: 10.1504/IJMR.2024.140290

International Journal of Manufacturing Research, 2024 Vol.19 No.2, pp.145 - 158

Accepted: 13 Nov 2023
Published online: 01 Aug 2024 *

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