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OBJECT DETECTION IN DEEP LEARNINGHaoyu Shi (8100614) 10 December 2019 (has links)
<p>Through the computing advance and GPU (Graphics Processing
Unit) availability for math calculation, the deep learning field becomes more
popular and prevalent. Object detection with deep learning, which is the part
of image processing, plays an important role in automatic vehicle drive and
computer vision. Object detection includes object localization and object
classification. Object localization involves that the computer looks through
the image and gives the correct coordinates to localize the object. Object
classification is that the computer classification targets into different
categories. The traditional image object detection pipeline idea is from
Fast/Faster R-CNN [32] [58]. The region proposal network
generates the contained objects areas and put them into classifier. The first
step is the object localization while the second step is the object
classification. The time cost for this pipeline function is not efficient.
Aiming to address this problem, You Only Look Once (YOLO) [4] network is born. YOLO is the
single neural network end-to-end pipeline with the image processing speed being
45 frames per second in real time for network prediction. In this thesis, the
convolution neural networks are introduced, including the state of art
convolutional neural networks in recently years. YOLO implementation details
are illustrated step by step. We adopt the YOLO network for our applications
since the YOLO network has the faster convergence rate in training and provides
high accuracy and it is the end to end architecture, which makes networks easy
to optimize and train. </p>
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Semantic Segmentation Using Deep Learning Neural ArchitecturesSarpangala, Kishan January 2019 (has links)
No description available.
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Segmentace cévního řečiště ve snímcích sítnice metodami hlubokého učení / Blood vessel segmentation in retinal images using deep learning approachesSerečunová, Stanislava January 2018 (has links)
This diploma thesis deals with the application of deep neural networks with focus on image segmentation. The theoretical part contains a description of deep neural networks and a summary of widely used convolutional architectures for segmentation of objects from the image. Practical part of the work was devoted to testing of an existing network architectures. For this purpose, an open-source software library Tensorflow, implemented in Python programming language, was used. A frequent problem incorporating the use of convolutional neural networks is the requirement on large amount of input data. In order to overcome this obstacle a new data set, consisting of a combination of five freely available databases was created. The selected U-net network architecture was tested by first modification of the newly created data set. Based on the test results, the chosen network architecture has been modified. By these means a new network has been created achieving better performance in comparison to the original network. The modified architecture is then trained on a newly created data set, that contains images of different types taken with various fundus cameras. As a result, the trained network is more robust and allows segmentation of retina blood vessels from images with different parameters. The modified architecture was tested on the STARE, CHASE, and HRF databases. Results were compared with published segmentation methods from literature, which are based on convolutional neural networks, as well as classical segmentation methods. The created network shows a high success rate of retina blood vessels segmentation comparable to state-of-the-art methods.
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Odhad kanálu v OFDM systémech pomocí deep learning metod / Utilization of deep learning for channel estimation in OFDM systemsHubík, Daniel January 2019 (has links)
This paper describes a wireless communication model based on IEEE 802.11n. Typical methods for channel equalisation and estimation are described, such as the least squares method and the minimum mean square error method. Equalization based on deep learning was used as well. Coded and uncoded bit error rate was used as a performance identifier. Experiments with topology of the neural network has been performed. Programming languages such as MATLAB and Python were used in this work.
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