Image defect detection algorithm based on deep learning

R A Sizyakin,A A Zelensky,A Pižurica,V V Voronin,N V Gapon

doi:10.1088/1757-899x/680/1/012041

R A Sizyakin, A A Zelensky + Show 3 more

Open Access

https://doi.org/10.1088/1757-899x/680/1/012041

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Abstract

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.

Highlights

Solution automatic defect detection problem is widely used in practice
This problem occurs when searching for defects in the road surface, the textile industry, as well as virtual restoration of archival photo images
To compare the effectiveness of detecting damage in the image, the proposed method is compared with methods based on machine learning: convolutional neural networks (CNN) and support vector machine method (SVM)

Summary

Introduction

Solution automatic defect detection problem is widely used in practice. This problem occurs when searching for defects in the road surface, the textile industry, as well as virtual restoration of archival photo images. In [1] for the detection of defects in the image using a method based on machine learning. For a mask with an estimated localization defects using morphological operations top and bottom hats to detect light and dark cracks.

Results

Conclusion