Automated enhancement and detection of stripe defects in large circular weft knitted fabrics
| Autor: | Kristina Simonis, Hanry Ham, Dorit Merhof, Raphael Kolk, Marcin Kopaczka |
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| Rok vydání: | 2016 |
| Předmět: |
010407 polymers
Engineering business.industry Matched filter Pipeline (computing) 020208 electrical & electronic engineering Feature extraction ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Image processing 02 engineering and technology Yarn 01 natural sciences 0104 chemical sciences Image (mathematics) Random forest Support vector machine visual_art 0202 electrical engineering electronic engineering information engineering visual_art.visual_art_medium Computer vision Artificial intelligence business |
| Zdroj: | ETFA |
| Popis: | Stripes are periodic defects that are difficult to detect during production even by experienced human inspectors. Therefore, we introduce an image processing method for automatically detecting stripe defects in circularly knitted fabric. We show how a barely visible defect can be optically enhanced to improve manual assessment as well as how descriptor-based image processing and machine learning can be used to allow automated stripe detection. Image enhancement is performed by applying gabor and matched filters to histogram-equalized fabric images. Subsequently, we extract image information with different descriptors (LBP, GLCM, HOG) and feed these into random forest and SVM classifiers. The full pipeline is validated by training and testing it on three sets of fabric produced with different knitting machines and parameter settings. Results show that the proposed enhancement combined with a statistics-based descriptor such as GLCM or HOG allows to train both tested classifiers with good classification rates of up to 98.9%. |
| Databáze: | OpenAIRE |
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