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Automatic detection of welding defects using the convolutional neural network
Author(s): Roman Sizyakin; Viacheslav Voronin; Nikolay Gapon; Aleksandr Zelensky; Aleksandra Pižurica
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Paper Abstract

Quality control of welded joints is an important step before commissioning of various types of metal structures. The main obstacles to the commissioning of such facilities are the areas where the welded joint deviates from acceptable defective standards. The defects of welded joints include non-welded, foreign inclusions, cracks, pores, etc. The article describes an approach to the detection of the main types of defects of welded joints using a combination of convolutional neural networks and support vector machine methods. Convolutional neural networks are used for primary classification. The support vector machine is used to accurately define defect boundaries. As a preprocessing in our work, we use the methods of morphological filtration. A series of experiments confirms the high efficiency of the proposed method in comparison with pure CNN method for detecting defects.

Paper Details

Date Published: 21 June 2019
PDF: 9 pages
Proc. SPIE 11061, Automated Visual Inspection and Machine Vision III, 110610E (21 June 2019); doi: 10.1117/12.2525643
Show Author Affiliations
Roman Sizyakin, Don State Technical Univ. (Russian Federation)
Viacheslav Voronin, Don State Technical Univ. (Russian Federation)
Moscow State Univ. of Technology “STANKIN” (Russian Federation)
Nikolay Gapon, Don State Technical Univ. (Russian Federation)
Aleksandr Zelensky, Moscow State Univ. of Technology “STANKIN” (Russian Federation)
Aleksandra Pižurica, Ghent Univ. (Belgium)


Published in SPIE Proceedings Vol. 11061:
Automated Visual Inspection and Machine Vision III
Jürgen Beyerer; Fernando Puente León, Editor(s)

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