online read us now
Paper details
Number 3 - September 2021
Volume 31 - 2021
A nested autoencoder approach to automated defect inspection on textured surfaces
Muhammed Ali Nur Oz, Ozgur Turay Kaymakci, Muharrem Mercimek
Abstract
In recent years, there has been a highly competitive pressure on industrial production. To keep ahead of the competition,
emerging technologies must be developed and incorporated. Automated visual inspection systems, which improve the overall
mass production quantity and quality in lines, are crucial. The modifications of the inspection system involve excessive
time and money costs. Therefore, these systems should be flexible in terms of fulfilling the changing requirements of high
capacity production support. A coherent defect detection model as a primary application to be used in a real-time intelligent
visual surface inspection system is proposed in this paper. The method utilizes a new approach consisting of nested autoencoders trained with defect-free and defect injected samples to detect defects. Making use of two nested autoencoders,
the proposed approach shows great performance in eliminating defects. The first autoencoder is used essentially for feature
extraction and reconstructing the image from these features. The second one is employed to identify and fix defects in the
feature code. Defects are detected by thresholding the difference between decoded feature code outputs of the first and the
second autoencoder. The proposed model has a 96% detection rate and a relatively good segmentation performance while
being able to inspect fabrics driven at high speeds.
Keywords
autoencoders, defect detection, automatic visual inspection, deep learning