Abstract
The design and development of surface defect detection and recognition systems for optical non-destructive testing (NDT) tasks is a complex, important and pressing problem today. Detection and classification of surface defects using Computer Vision (CV) and Machine Learning (ML) algorithms serves as an effective tool for production process control, quality management and increasing the profitability of enterprises. In this paper, Deep Learning (DL) and Computer Vision (CV) techniques are used to solve the problem of surface defect detection. Using Convolutional Neural Network (CNN), detection and recognition of various defects is carried out to improve production standards and process efficiency. The outcome of this paper is a comparative analysis of DL models and the selection of an algorithm designed to find and classify defects online. The application of such CNN models could allow the creation of a tool that considerably facilitates human work.
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