TFR: Texture Defect Detection with Fourier Transform using Normal Reconstructed Template of Simple Autoencoder
This addresses the problem of detecting defects in real-world textures for the manufacturing industry, but it appears incremental as it builds on existing methods like Fourier transforms and autoencoders.
The study tackled texture defect detection in manufacturing by combining a simple autoencoder with Fourier transform analysis, achieving effective and accurate results as demonstrated in experiments.
Texture is an essential information in image representation, capturing patterns and structures. As a result, texture plays a crucial role in the manufacturing industry and is extensively studied in the fields of computer vision and pattern recognition. However, real-world textures are susceptible to defects, which can degrade image quality and cause various issues. Therefore, there is a need for accurate and effective methods to detect texture defects. In this study, a simple autoencoder and Fourier transform are employed for texture defect detection. The proposed method combines Fourier transform analysis with the reconstructed template obtained from the simple autoencoder. Fourier transform is a powerful tool for analyzing the frequency domain of images and signals. Moreover, since texture defects often exhibit characteristic changes in specific frequency ranges, analyzing the frequency domain enables effective defect detection. The proposed method demonstrates effectiveness and accuracy in detecting texture defects. Experimental results are presented to evaluate its performance and compare it with existing approaches.