Open AccessImage defect detection algorithm based on deep learning
Author(s) -
Roman Sizyakin,
В. В. Воронин,
Nikolay Gapon,
Alexander A. Zelensky,
Aleksandra Pižurica
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/680/1/012041
Subject(s) - computer science , reliability (semiconductor) , field (mathematics) , artificial intelligence , image (mathematics) , algorithm , artificial neural network , deep learning , range (aeronautics) , computer vision , engineering , mathematics , power (physics) , physics , quantum mechanics , pure mathematics , aerospace engineering
In this paper proposed a system for automatic defects detection in images. The solution to this problem is widely used in practice. Automatic detection is found in the challenge of detecting defects on the road surface, in the textile industry, as well as virtual restoration of archival photo images. The solution to this range of problems allows speeding up work in these areas, and in some cases, completely solving. To solve the first two problems (search for defects on the pavement and textiles), it is enough to create a mask that localizes defects in the image with maximum reliability, while photo restoration requires additional algorithms to restore the detected damaged areas. The proposed method is based on the latest achievements in the field of machine learning and allows solve the main disadvantages of traditional methods. Automatic defect detection is performed using a neural network with compound descriptor. A series of experiments confirmed the high efficiency of the proposed method in comparison with traditional methods for detecting defects.