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Defects classification
Benda, Jan ; Richter, Miloslav (referee) ; Honec, Peter (advisor)
The thesis deals with a concept and creation of classifiers of defects found on continuous production lines. The first part presents an overview of methods used for image classification and a analysis of defects. The main part of the thesis consist of a description of created classifier interface and graphical user interface for classifier. The last part sums up reliability of each implemented classifer.
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Automatic Image Analysis for Production Quality Control of Textile
Sýkorová, Tereza ; Dobeš, Petr (referee) ; Zemčík, Pavel (advisor)
This work deals with the classification of defects that occur in the production of nonwovens. The defect classification task is part of a system for automatic production quality control. The goal is to implement a method that will classify problematic defect classes with sufficient accuracy. That was achieved using convolutional neural networks (CNN). The best results were achieved by the EfficientNet network, which had an accuracy of 81% when evaluated by cross-validation on an available dataset. Within the work, a number of experiments are performed, which are focused on the modification of input data. The influence of the shape and composition of the input images on the final classification is examined. A CNN model was also implemented, which uses additional information for classification in addition to the image.
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Automatic Image Analysis for Production Quality Control of Textile
Sýkorová, Tereza ; Dobeš, Petr (referee) ; Zemčík, Pavel (advisor)
This work deals with the classification of defects that occur in the production of nonwovens. The defect classification task is part of a system for automatic production quality control. The goal is to implement a method that will classify problematic defect classes with sufficient accuracy. That was achieved using convolutional neural networks (CNN). The best results were achieved by the EfficientNet network, which had an accuracy of 81% when evaluated by cross-validation on an available dataset. Within the work, a number of experiments are performed, which are focused on the modification of input data. The influence of the shape and composition of the input images on the final classification is examined. A CNN model was also implemented, which uses additional information for classification in addition to the image.
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|
|
Defects classification
Benda, Jan ; Richter, Miloslav (referee) ; Honec, Peter (advisor)
The thesis deals with a concept and creation of classifiers of defects found on continuous production lines. The first part presents an overview of methods used for image classification and a analysis of defects. The main part of the thesis consist of a description of created classifier interface and graphical user interface for classifier. The last part sums up reliability of each implemented classifer.
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