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Detection of Boxes in Image
Soroka, Matej ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The aim of this work is to experiment and evaluate different approaches of computer vision with the aim of automatic detection of boxes-blocks in the image, for this purpose, approaches based on neural networks were used in the solution. Experiments were performed with classification using our own data set, classification using our own convolutional neural network, detection using a window, YOLO detector and in the last part a proposal for improvement using U-net and MirrorNet networks.
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Detection of Boxes in Image
Soroka, Matej ; Zlámal, Adam (referee) ; Herout, Adam (advisor)
The aim of this work is to experiment and evaluate algorithms with different approaches to computer vision in order to automatically detect boxes-blocks in the image. To this end, neural network-based approaches were used in the solution. Experiments were performed with classification using our own data set, classification using our own convolutional neural network, detection using a window, YOLO detector and in the final iteration the use of U-net network for detection of boxes in the image.
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Detection of Boxes in Image
Soroka, Matej ; Zlámal, Adam (referee) ; Herout, Adam (advisor)
The aim of this work is to experiment and evaluate algorithms with different approaches to computer vision in order to automatically detect boxes-blocks in the image. To this end, neural network-based approaches were used in the solution. Experiments were performed with classification using our own data set, classification using our own convolutional neural network, detection using a window, YOLO detector and in the final iteration the use of U-net network for detection of boxes in the image.
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Detection of Boxes in Image
Soroka, Matej ; Bartl, Vojtěch (referee) ; Herout, Adam (advisor)
The aim of this work is to experiment and evaluate different approaches of computer vision with the aim of automatic detection of boxes-blocks in the image, for this purpose, approaches based on neural networks were used in the solution. Experiments were performed with classification using our own data set, classification using our own convolutional neural network, detection using a window, YOLO detector and in the last part a proposal for improvement using U-net and MirrorNet networks.
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