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Recognition of Driving Lane Borders in Video from On-Board Camera
Fridrich, David ; Kohút, Jan (referee) ; Herout, Adam (advisor)
This paper talks about lane detection. Specifically custom generator of synthetic images, usage during training of neural networks, testing on convolutional neural network (CNN) UNet model and possibilities of extension of this model to SALMnet (Structure-Aware Lane Marking Detection Network) via addding SGCA module (semantic-guided channel attention) and PDC module (pyramid deformable convolution). Training results from synthetic datasets show very accurate results, reaching around 95\,\% in accuracy (even 99\,\% for easier images). Trainings with real datasets show lower accuracy, depending on the difficulty of the dataset itself. TuSimple has easier and clearer images and reaches about 62\,\%. CuLane is much more complex and results show accuracy around 37\,\%.
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Combination of laser spectroscopy methods for chemical analysis
Holub, Daniel ; Novotný, Karel (referee) ; Pořízka, Pavel (advisor)
The topic of this Master’s thesis is combination of laser spectroscopic methods. LIBS and Raman spectroscopy were chosen for the combination. This combination is applied to plastic identification and separation as a mean to automate sorting of plastic waste. Data handling was done via different methods of computer learning algorithms scripted in R language. Plastic sorting accuracy over 90 % was reached thanks to the combination of chosen methods. This work also addresses some issues implied by combination of two different methods.
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Recognition of Driving Lane Borders in Video from On-Board Camera
Fridrich, David ; Kohút, Jan (referee) ; Herout, Adam (advisor)
This paper talks about lane detection. Specifically custom generator of synthetic images, usage during training of neural networks, testing on convolutional neural network (CNN) UNet model and possibilities of extension of this model to SALMnet (Structure-Aware Lane Marking Detection Network) via addding SGCA module (semantic-guided channel attention) and PDC module (pyramid deformable convolution). Training results from synthetic datasets show very accurate results, reaching around 95\,\% in accuracy (even 99\,\% for easier images). Trainings with real datasets show lower accuracy, depending on the difficulty of the dataset itself. TuSimple has easier and clearer images and reaches about 62\,\%. CuLane is much more complex and results show accuracy around 37\,\%.
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Combination of laser spectroscopy methods for chemical analysis
Holub, Daniel ; Novotný, Karel (referee) ; Pořízka, Pavel (advisor)
The topic of this Master’s thesis is combination of laser spectroscopic methods. LIBS and Raman spectroscopy were chosen for the combination. This combination is applied to plastic identification and separation as a mean to automate sorting of plastic waste. Data handling was done via different methods of computer learning algorithms scripted in R language. Plastic sorting accuracy over 90 % was reached thanks to the combination of chosen methods. This work also addresses some issues implied by combination of two different methods.
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