TFR: Texture Defect Detection with Fourier Transform using Normal Reconstructed Template of Simple Autoencoder

TL;DR

This paper introduces a novel texture defect detection method combining Fourier transform analysis with a simple autoencoder's reconstructed template, demonstrating improved accuracy in identifying defects in textures.

Contribution

The study presents a new approach that integrates Fourier transform with autoencoder-based templates for more effective texture defect detection.

Findings

Effective detection of texture defects demonstrated.

Outperforms existing methods in accuracy.

Utilizes frequency domain analysis for improved results.

Abstract

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.