Overview of Application Based Approach For Automatic Texture Defect
Application-based approach for automatic texture defect recognition on synthetic surfaces Abstract: Synthetic surfaces, in particular polymer structures, which are used for electronic components, have to be inspected in industrial processes. Polymers show some specific surface characteristics.
Application-based approach for automatic texture defect recognition on synthetic surfaces October 2008 DOI: 10.1109/ETFA.2008.4638397 Source IEEE Xplore
Key Details About Application Based Approach For Automatic Texture Defect
This chapter describes an approach for a Fuzzy-Pattern-Classifier Sensor Fusion Model based on a general framework and shows how such systems can be used to enhance the stability of inspection and condition results for user convenience while improving machine reliability.
Application-based approach for automatic texture defect recognition on synthetic surfaces Marcus Niederhöfer Phoenix Contact GmbH & Co. KG Flachsmarktstraße 8 32825 Blomberg, Germany ...
(PDF) Application
The Modified Fuzzy Pattern Classifier (MFPC) excels in classifying texture defects in synthetic surfaces. Feature extraction employs sum-and-difference histograms for robust statistical analysis of surface textures . The approach enables defect detection without extensive reference learning data, focusing on textural consistency. Median- based feature vectors adaptively represent the texture ...
Application-based Approach for Automatic Texture Defect Recognition on Synthetic Surfaces
Automatic defect detection of texture surface with an efficient texture ...
We conducted experiments on a milled metal surface defect dataset and an open data set containing various textured backgrounds to evaluate the performance of our method. Compared with other convolutional neural network approaches, the results demonstrate the superiority of the proposed method.
Texture defect detection finds its uses in the textile industry for automatic fabric inspection, and also has the potential for contributing to modern day video-surveillance and abnormality detection. Texture defect detection techniques are broadly classified into statistical methods that are based on gray-level co-occurrence matrices and the transform methods, an example being Gabor filters ...
Deep Learning
To develop a deep learning- based approach for automatic defect defection, datasets of welded and composite structures were prepared. We first needed to develop an approach for automatic defect detection that would work with one type of weld shape before expanding it to apply to other types of weld shapes.
There are two major issues in defect inspection like defect identification and classification in fabric. Automatic identification of defects is quite important in the current scenario. For enhancing the quality of the fabric, this paper proposes a Texture Defect Detection (TDD) algorithm.