0 citations0 references

Fabric Defect Detection System Using Stacked Convolutional Denoising Auto-Encoders Trained with Synthetic Defect Data

Applied Sciences2020Vol. 10(7), pp. 2511–2511

Citations Over TimeTop 10% of 2020 papers

Abstract

As defect detection using machine vision is diversifying and expanding, approaches using deep learning are increasing. Recently, there have been much research for detecting and classifying defects using image segmentation, image detection, and image classification. These methods are effective but require a large number of actual defect data. However, it is very difficult to get a large amount of actual defect data in industrial areas. To overcome this problem, we propose a method for defect detection using stacked convolutional autoencoders. The autoencoders we proposed are trained by using only non-defect data and synthetic defect data generated by using the characteristics of defect based on the knowledge of the experts. A key advantage of our approach is that actual defect data is not required, and we verified that the performance is comparable to the systems trained using real defect data.

Related Papers

→ A Rotary Encoder Based on Magneto-Optical Storage(2005)18 cited
Design and Realization of RS Encoder Based on FPGA(2009)
→ Area and Energy Efficient QCA Based Compact Serial Concatenated Convolutional Code Encoder(2022)
→ A(2023)