20.500.12556/DKUM-80246 Prepoznavanje objektov iz satelitskih slik z metodami globokega učenja na vgrajeni napravi diplomsko delo Object detection in satellite images with deep learning methods on embedded system V diplomskem delu rešujemo problem prepoznavanja prometa iz satelitskih slik. Cilj je bil uporabiti metode globokega učenja, pognati modele na izbranih vgrajenih napravah in doseči povprečno natančnost vsaj 75 % pri hitrosti izvajanja 5 sličic na sekundo. Za eksperiment uporabimo modela Faster R-CNN in SSD iz knjižnic Detectron2 ter TensorFlow Object Detection API. Fazi učenja in testiranja izvedemo na satelitskih slikah baze podatkov xView, katere predhodno razdelimo na učno in testno množico. Na učni množici izvedemo tudi bogatenje slik. Naučene modele preizkusimo na grafičnih karticah Nvidia GeForce GTX 970 ter Nvidia Titan X Pascal, na procesorju Intel Core i7-4790 in na vgrajenih napravah Intel Neural Compute Stick 2 ter Nvidia Jetson TX2. Preizkuse izvedemo s pomočjo skript napisanih v programskem jeziku Python3. Te izvozijo modele v posebno zamrznjeno stanje, jih optimizirajo za izvajanje na izbrani napravi in izmerijo njegovo hitrost ter natančnost. Najvišjo povprečno natančnost 37,33 % dosežemo z modelom Faster R-CNN iz knjižnice Detectron2. Z modelom SSD iz knjižnice TensorFlow Object Detection API na grafični kartici Nvidia GeForce GTX 970 dosežemo povprečno hitrost izvajanja 84,5 sličic na sekundo. Demonstrirana rešitev v diplomskem delu je primerna za izvajanje na vgrajenih napravah, a žal ni dovolj natančna. Za doseganje boljših rezultatov moramo našo rešitev izvajati na hitrejši strojni opremi, ki podpira večje ter s tem natančnejše modele. The problem of detecting traffic in satellite images is being solved in this thesis. The goal was to use deep learning methods, run the models on selected embedded systems and achieve a mean average precision of at least 75% at a processing speed of 5 frames per second. Models Faster R-CNN and SSD implemented in Detectron2 and TensorFlow Object Detection API libraries were used. The training and testing phases were done on satellite images from the xView dataset, which were previously separated into training and testing splits. Image augmentation was also done on the training split. Trained models were tested on Nvidia GeForce GTX 970 and Nvidia Titan X Pascal GPUs, on the Intel Core i7-4790 CPU and on Intel Neural Compute Stick 2 and Nvidia Jetson TX2 embedded systems. The tests were done with scripts written in the programming language Python3. The models were exported to a frozen state, optimized for the chosen hardware, and their speed and accuracy was measured by the scripts. The highest mean average precision 37.33% was achieved by the Faster R-CNN model implemented in the Detectron2. Also, a processing speed of 84.5 frames per second was achieved on the Nvidia GeForce GTX 970 GPU with the model SSD implemented in the TensorFlow Object Detection API library. The solution demonstrated in the thesis is suitable for running on embedded systems, but unfortunately it is not accurate enough. To achieve better results, our solution needs to be run on faster hardware that supports larger and thus more accurate models. strojno učenje globoko učenje vgrajene naprave prepoznavanje objektov satelitske slike računalniški vid machine learning deep learning embedded systems object detection satellite images computer vision true true false [M. Domajnko] Slovenski jezik Angleški jezik Diplomsko delo/naloga 2021-09-03 11:35:41 2021-10-18 12:39:49 2022-08-09 22:40:48 0000-00-00 00:00:00 2021 0 Maribor 0 Maribor X, 40 str. 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004.85:004.932.75\'1(043.2) 87069187 UN_Domajnko_Martin_2021.pdf UN_Domajnko_Martin_2021.pdf 1 9B42ED11A910DED7947D4352C3465ED0 ceed79db406e81593362d4b626929784714ad586ddacef0853ac7e0cf35a0b85 3ddcd34c-0ca7-11ec-a1da-00155d581298 20.500.12556/dkum/11827250-dd72-45f3-8f61-97dbdb6049cb https://dk.um.si/Dokument.php?lang=slv&id=153031 Fakulteta za elektrotehniko, računalništvo in informatiko 0 0 0