Journal of Optoelectronics · Laser, Volume. 36, Issue 7, 705(2025)
Research on weld defect recognition by integrating joint norm and principal component analysis
[1] [1] ACHERGEE B. Laser transmission welding of polymers-a review on process fundamentals, material attributes, weld ability, and welding techniques[J]. Journal of Manufacturing Processes, 2020, 60:227-246.
[2] [2] XU X, LI X. Research on surface defect detection algorithm of pipeline weld based on YOLOv7[J]. Scientific Reports, 2024, 14(1):1881.
[3] [3] KERMORGANT O. A magnetic climbing robot to perform autonomous welding in the shipbuilding industry[J]. Robotics and Computer Integrated Manufacturing, 2018, 53:178-186.
[4] [4] HOU W, ZHANG D, WEI Y, et al. Review on computer aided weld defect detection from radiography images[J]. Applied Sciences, 2020, 10(5):1878.
[5] [5] CARVALHO A, REBELLO J, SOUZA M, et al. Reliability of non-destructive test techniques in the inspection of pipelines used in the oil industry[J]. International Journal of Pressure Vessels and Piping, 2008, 85(11):745-751.
[8] [8] WEI P L, SHENG Q S, HAI Y C, et al. X-ray weld defect detection based on AFRCNN[J]. Welding in the World, 2022, 66(6):1165-1177.
[9] [9] HOU W, WEI Y, JIN Y, et al. Deep features based on a DCNN model for classifying imbalanced weld flaw types[J]. Measurement, 2018, 131:482-489.
[10] [10] ZHANG Z F, WEN G R, CHEN S B. Weld image deep learning-based online defects detection using convolutional neural networks for AI alloy in robotic arc welding[J]. Journal of Manufacturing Processes, 2019, 45:208-216.
[11] [11] SATISH S, SHITAL C. Enhancing weld defect detection and classification with MDCBNet: a multiscale dense cross block network for improved explain ability[J]. NDT and E International, 2024, 142:103029.
[12] [12] AJMI C, ZAPATA J, MARTNEZ-LVAREZ J J, et al. Using deep learning for defect classification on a small weld X-ray image dataset[J]. Journal of Nondestructive Evaluation, 2020, 39(3):661-667.
[14] [14] YANG J, ZHANG D D, YANG J Y. Constructing PCA baseline algorithms to reevaluate ICA based face recognition performance[J]. IEEE Transactions on Cybernetics, 2007, 37(4):1015-1021.
[15] [15] HUANG J, YUEN C P, CHEN S W, et al. Choosing parameters of kernel subspace LDA for recognition of face images under pose and illumination variations[J]. IEEE Transactions on Cybernetics, 2007, 37(4):847-862.
[18] [18] YANG J, DAVID Z, FRANGI A F, et al. Two dimensional PCA: a new approach to appearance-based face representation and recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(1):131-137.
[19] [19] LI X, PANG Y, YUAN Y. L1-norm-based 2DPCA[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 40(4):1170-1175.
[20] [20] GAO Q X, XU S, CHEN F, et al. R1-2-DPCA and face recognition[J]. IEEE Transactions on Cybernetics, 2018, 49(4):1212-1223.
[21] [21] GAO Q X, LAN M, YANG L, et al. Angle-2DPCA: a new formulation for 2DPCA[J]. IEEE Transactions on Cybernetics, 2018, 48(5):1672-1678.
Get Citation
Copy Citation Text
XU Shengqi, ZHANG Chao, WANG Xiaofeng. Research on weld defect recognition by integrating joint norm and principal component analysis[J]. Journal of Optoelectronics · Laser, 2025, 36(7): 705
Category:
Received: Apr. 9, 2024
Accepted: Jun. 24, 2025
Published Online: Jun. 24, 2025
The Author Email: WANG Xiaofeng (1056470187@qq.com)