has been cited by the following article(s):
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[1]
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Developing YOLOv5 for weld flaws detection and localization in gamma radiography images based on attention mechanisms
Multimedia Tools and Applications,
2026
DOI:10.1007/s11042-026-21272-z
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[2]
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Review of Nondestructive Testing Methods for Aerospace Composite Materials
Journal of Computers, Mechanical and Management,
2024
DOI:10.57159/gadl.jcmm.3.1.240117
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[3]
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Welded Pipe Defect Detection Enhancement Using Regularized Kernel Estimation-Based Image Processing in Radiographic Testing
Russian Journal of Nondestructive Testing,
2022
DOI:10.1134/S1061830922080046
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[4]
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Automatic detection of welding defects using the convolutional neural network
Automated Visual Inspection and Machine Vision III,
2019
DOI:10.1117/12.2525643
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[5]
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Classification of Welding Flaws in Gamma Radiography Images Based on Multi-scale Wavelet Packet Feature Extraction Using Support Vector Machine
Journal of Nondestructive Evaluation,
2015
DOI:10.1007/s10921-015-0305-9
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[6]
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DWT Based Automated Weld Pool Detection and Defect Characterisation from Weld Radiographs
Advanced Materials Research,
2014
DOI:10.4028/www.scientific.net/AMR.984-985.573
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