defect detection, computer vision, generalized method of least modules, GMLM
Abstract
The article presents an algorithm for detecting defects in glass insulators using computer vision. Insulators, which are key elements of electrical networks, are subject to various defects, such as bubbles, chips and deformations. Such damage can significantly reduce the service life of insulators. In traditional production conditions, these defects are detected manually, which reduces productivity and increases the likelihood of human factor-based errors.
To solve the problem related to manual control restraints, the authors developed an algorithm based on the use of a neural network. The main task of the algorithm is to automatically identify defects that have a significant impact on the mechanical and electrical insulation properties of products.
The authors collected a data set for training the neural network and supplemented it with generated images to increase the sample of the location and shape of the considered defects. The paper describes in detail the steps of data preprocessing, including augmenting the contrast to increase the detectability of defects and reducing noise. Fragmenting is described to process defects of various sizes and shapes. Such fragmenting allows detecting defects of different sizes relative to the insulator size.
Author Biographies
Anton Veniaminovich Korzhov, South Ural State University, Chelyabinsk, Russian Federation
Dr. Sc. (Engineering), Associate Professor, Professor of the Power Plants, Networks and Power Supply Systems Department, Vice-Rector for Research
Vladimir Anatol'evich Surin, South Ural State University, Chelyabinsk, Russian Federation
Cand. Sc. (Physics and Mathematics), Senior Lecturer, Mathematics and Programming Department
Mariya Aleksandrovna Cheskidova, South Ural State University, Chelyabinsk, Russian Federation
Post-graduate Student, Lecturer, Mathematics and Programming Department
Petr Vladimirovich Lonzinger, South Ural State University, Chelyabinsk, Russian Federation
Cand. Sc. (Engineering), Associate Professor, Power Plants, Networks and Power Supply Systems Department
Valeriy Ivanovich Safonov, South Ural State University, Chelyabinsk, Russian Federation
Cand. Sc. (Physics and Mathematics), Associate Professor, Power Plants, Networks and Power Supply Systems Department
Kirill Nikolaevich Belov, South Ural State University, Chelyabinsk, Russian Federation
Post-graduate Student, Assistant, Optoinformatics Department
