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Detection of significant events in systems baased on phase OTDR
Makówka, David ; Petyovský, Petr (referee) ; Valach, Soběslav (advisor)
This diploma thesis concerns the design, implementation and testing of a system that classifies events captured using optic fiber along a perimeter of guarded objects. A theoretical part introduces physical principles, main structures of measuring systems, methods of measuring, data format, pre-processing options and classification using convolutional neural networks. A practical part describes implementation of a software for convolutional neural networks training and testing, process of samples extraction from measured data, its annotation and conversion to format required by neural networks. Results of measured data analysis and results of achieved classification accuracy using convolutional neural networks for both post processing of measured data and for deployment of neural network into real time processing system are presented.
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Image based flower recognition
Jedlička, František ; Kříž, Petr (referee) ; Přinosil, Jiří (advisor)
This paper is focus on flowers recognition in an image and class classification. Theoretical part is focus on problematics of deep convolutional neural networks. The practical part if focuse on created flowers database, with which it is further worked on. The database conteins it total 13000 plant pictures of 26 spicies as cornflower, violet, gerbera, cha- momile, cornflower, liverwort, hawkweed, clover, carnation, lily of the valley, marguerite daisy, pansy, poppy, marigold, daffodil, dandelion, teasel, forget-me-not, rose, anemone, daisy, sunflower, snowdrop, ragwort, tulip and celandine. Next is in the paper described used neural network model Inception v3 for class classification. The resulting accuracy has been achieved 92%.
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Detection of significant events in systems baased on phase OTDR
Makówka, David ; Petyovský, Petr (referee) ; Valach, Soběslav (advisor)
This diploma thesis concerns the design, implementation and testing of a system that classifies events captured using optic fiber along a perimeter of guarded objects. A theoretical part introduces physical principles, main structures of measuring systems, methods of measuring, data format, pre-processing options and classification using convolutional neural networks. A practical part describes implementation of a software for convolutional neural networks training and testing, process of samples extraction from measured data, its annotation and conversion to format required by neural networks. Results of measured data analysis and results of achieved classification accuracy using convolutional neural networks for both post processing of measured data and for deployment of neural network into real time processing system are presented.
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Image based flower recognition
Jedlička, František ; Kříž, Petr (referee) ; Přinosil, Jiří (advisor)
This paper is focus on flowers recognition in an image and class classification. Theoretical part is focus on problematics of deep convolutional neural networks. The practical part if focuse on created flowers database, with which it is further worked on. The database conteins it total 13000 plant pictures of 26 spicies as cornflower, violet, gerbera, cha- momile, cornflower, liverwort, hawkweed, clover, carnation, lily of the valley, marguerite daisy, pansy, poppy, marigold, daffodil, dandelion, teasel, forget-me-not, rose, anemone, daisy, sunflower, snowdrop, ragwort, tulip and celandine. Next is in the paper described used neural network model Inception v3 for class classification. The resulting accuracy has been achieved 92%.
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