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Graffiti Tags Detection Mobile Application
Chovaneček, Přemysl ; Teuer, Lukáš (referee) ; Špaňhel, Jakub (advisor)
Thesis focuses on the object recognition of images, using the principles of artificial intelligence. It solves the signature detection of authors in the field of art called graffiti. It concerns about basic problematic of this field, it also points to the use of computer vision followed by practical application on mobile devices, specifically on the Android platform. The selected neural network models was the ssdMobileNet_v2 . The trained model achieves mAP accuracy of 73.5% meanwhile the IoU was set to 0.6. After the quantization process, the accuracy was reduced to 68.5%. The mobile application provides real-time detection and several other necessary functions for localization and data collection.
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Image Compression with Neural Networks
Teuer, Lukáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This document describes image compression using different types of neural networks. Features of neural networks like convolutional and recurrent networks are also discussed here. The document contains detailed description of various neural network architectures and their inner workings. In addition, experiments are carried out on various neural network structures and parameters in order to find the most appropriate properties for image compression. Also, there are proposed new concepts for image compression using neural networks that are also immediately tested. Finally, a network of the best concepts and parts discovered during experimentation is designed.
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Deep Learning for Object Detection
Pitoňák, Radoslav ; Dobeš, Petr (referee) ; Teuer, Lukáš (advisor)
This thesis analyzes different object detection methods which are based on deep neural networks. In the beginning, the convolutional neural networks are described and commonly used object detection methods are compared. In the following parts, the proposal and implementation of the object detection model trained on the specific dataset are described. In conclusion, the achieved results of this model are discussed and compared with the results of other methods.
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Deep Learning for Object Detection
Paníček, Andrej ; Herout, Adam (referee) ; Teuer, Lukáš (advisor)
This work deals with the object detection using deep neural networks. As part of the solution, I modified, implemented and trained the well-known model of cascade neural networks MTCNN so that it could perform the detection of traffic signs. The training data was generated from GTSRB and GTSDB data sets. MTCNN showed solid performance on the evaluation data, where the detection accuracy reached 97.8 %.
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Graffiti Tags Detection Mobile Application
Chovaneček, Přemysl ; Teuer, Lukáš (referee) ; Špaňhel, Jakub (advisor)
Thesis focuses on the object recognition of images, using the principles of artificial intelligence. It solves the signature detection of authors in the field of art called graffiti. It concerns about basic problematic of this field, it also points to the use of computer vision followed by practical application on mobile devices, specifically on the Android platform. The selected neural network models was the ssdMobileNet_v2 . The trained model achieves mAP accuracy of 73.5% meanwhile the IoU was set to 0.6. After the quantization process, the accuracy was reduced to 68.5%. The mobile application provides real-time detection and several other necessary functions for localization and data collection.
|
|
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Deep Learning for Object Detection
Pitoňák, Radoslav ; Dobeš, Petr (referee) ; Teuer, Lukáš (advisor)
This thesis analyzes different object detection methods which are based on deep neural networks. In the beginning, the convolutional neural networks are described and commonly used object detection methods are compared. In the following parts, the proposal and implementation of the object detection model trained on the specific dataset are described. In conclusion, the achieved results of this model are discussed and compared with the results of other methods.
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|
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Deep Learning for Object Detection
Paníček, Andrej ; Herout, Adam (referee) ; Teuer, Lukáš (advisor)
This work deals with the object detection using deep neural networks. As part of the solution, I modified, implemented and trained the well-known model of cascade neural networks MTCNN so that it could perform the detection of traffic signs. The training data was generated from GTSRB and GTSDB data sets. MTCNN showed solid performance on the evaluation data, where the detection accuracy reached 97.8 %.
|
|
|
Image Compression with Neural Networks
Teuer, Lukáš ; Sochor, Jakub (referee) ; Hradiš, Michal (advisor)
This document describes image compression using different types of neural networks. Features of neural networks like convolutional and recurrent networks are also discussed here. The document contains detailed description of various neural network architectures and their inner workings. In addition, experiments are carried out on various neural network structures and parameters in order to find the most appropriate properties for image compression. Also, there are proposed new concepts for image compression using neural networks that are also immediately tested. Finally, a network of the best concepts and parts discovered during experimentation is designed.
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