Dissertations / Theses on the topic 'Deep learning, convolutional neural networks, classification, object detection'
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Lidberg, Love. "Object Detection using deep learning and synthetic data." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-150555.
Full textMarko, Arsenović. "Detekcija bolesti biljaka tehnikama dubokog učenja." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2020. https://www.cris.uns.ac.rs/record.jsf?recordId=114816&source=NDLTD&language=en.
Full textThe research presented in this thesis was aimed at developing a novel method based on deep convolutional neural networks for automated plant disease detection. Based on current available literature, specialized two-phased deep neural network method introduced in the experimental part of thesis solves the limitations of state-of-the-art plant disease detection methods and provides the possibility for a practical usage of the newly developed model. In addition, a new dataset was introduced, that has more images of leaves than other publicly available datasets, also GAN based augmentation approach on leaves images is experimentally confirmed.
Hřebíček, Zdeněk. "Klasifikace obrazů s pomocí hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2016. http://www.nusl.cz/ntk/nusl-241140.
Full textNorrstig, Andreas. "Visual Object Detection using Convolutional Neural Networks in a Virtual Environment." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156609.
Full textDickens, James. "Depth-Aware Deep Learning Networks for Object Detection and Image Segmentation." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42619.
Full textTang, Yuxing. "Weakly supervised learning of deformable part models and convolutional neural networks for object detection." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC062/document.
Full textIn this dissertation we address the problem of weakly supervised object detection, wherein the goal is to recognize and localize objects in weakly-labeled images where object-level annotations are incomplete during training. To this end, we propose two methods which learn two different models for the objects of interest. In our first method, we propose a model enhancing the weakly supervised Deformable Part-based Models (DPMs) by emphasizing the importance of location and size of the initial class-specific root filter. We first compute a candidate pool that represents the potential locations of the object as this root filter estimate, by exploring the generic objectness measurement (region proposals) to combine the most salient regions and “good” region proposals. We then propose learning of the latent class label of each candidate window as a binary classification problem, by training category-specific classifiers used to coarsely classify a candidate window into either a target object or a non-target class. Furthermore, we improve detection by incorporating the contextual information from image classification scores. Finally, we design a flexible enlarging-and-shrinking post-processing procedure to modify the DPMs outputs, which can effectively match the approximate object aspect ratios and further improve final accuracy. Second, we investigate how knowledge about object similarities from both visual and semantic domains can be transferred to adapt an image classifier to an object detector in a semi-supervised setting on a large-scale database, where a subset of object categories are annotated with bounding boxes. We propose to transform deep Convolutional Neural Networks (CNN)-based image-level classifiers into object detectors by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We have evaluated both our approaches extensively on several challenging detection benchmarks, e.g. , PASCAL VOC, ImageNet ILSVRC and Microsoft COCO. Both our approaches compare favorably to the state-of-the-art and show significant improvement over several other recent weakly supervised detection methods
Schennings, Jacob. "Deep Convolutional Neural Networks for Real-Time Single Frame Monocular Depth Estimation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-336923.
Full textKastberg, Maria. "Using Convolutional Neural Networks to Detect People Around Wells in South Sudan." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160325.
Full textRönnqvist, Johannes, and Johannes Sjölund. "A Deep Learning Approach to Detection and Classification of Small Defects on Painted Surfaces : A Study Made on Volvo GTO, Umeå." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160194.
Full textI den här rapporten visar vi att modeller av typen convolutional neural networks, tillsammans med phase-measuring deflektometri, kan hitta och klassificera defekter på målade ytor med hög precision, även jämfört med erfarna operatörer. Vidare visar vi vilka databehandlingsåtgärder som ökar modellernas prestanda. Vi ser att standardisering ökar modellernas klassificeringsförmåga. Vi visar att städning av data genom ommärkning och borttagning av felaktiga bilder förbättrar klassificeringsförmågan och särskilt modellernas förmåga att särskilja mellan olika typer av defekter. Vi visar att översampling kan vara en metod för att förbättra precisionen genom att öka och balansera datamängden genom att förändra och duplicera befintliga observationer. Slutligen finner vi att kombinera flera bilder med olika mönster ökar modellernas klassificeringsförmåga väsentligt. Vårt föreslagna tillvägagångssätt har visat sig fungera bra i realtid inom en produktionsmiljö. En automatiserad kvalitetskontroll av de målade ytorna på Volvos lastbilshytter kan ge stora fördelar med avseende på kostnad och kvalitet. Den automatiska kvalitetskontrollen kan ge data för en rotorsaksanalys och ett snabbt och effektivt alarmsystem. Detta kan väsentligt effektivisera produktionen och samtidigt minska kostnader och fel i produktionen. Korrigeringar och optimering av processerna kan göras i tidigare skeden och med högre precision än idag.
Melcherson, Tim. "Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146.
Full textLamberti, Lorenzo. "A deep learning solution for industrial OCR applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19777/.
Full textDobiš, Lukáš. "Detekce osob a hodnocení jejich pohlaví a věku v obrazových datech." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-413019.
Full textMarko, Peter. "Detekce objektů v laserových skenech pomocí konvolučních neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445509.
Full text(6622538), Liming Wu. "Biomedical Image Segmentation and Object Detection Using Deep Convolutional Neural Networks." Thesis, 2019.
Quick and accurate segmentation and object detection of the biomedical image is the starting point of most disease analysis and understanding of biological processes in medical research. It will enhance drug development and advance medical treatment, especially in cancer-related diseases. However, identifying the objects in the CT or MRI images and labeling them usually takes time even for an experienced person. Currently, there is no automatic detection technique for nucleus identification, pneumonia detection, and fetus brain segmentation. Fortunately, as the successful application of artificial intelligence (AI) in image processing, many challenging tasks are easily solved with deep convolutional neural networks. In light of this, in this thesis, the deep learning based object detection and segmentation methods were implemented to perform the nucleus segmentation, lung segmentation, pneumonia detection, and fetus brain segmentation. The semantic segmentation is achieved by the customized U-Net model, and the instance localization is achieved by Faster R-CNN. The reason we choose U-Net is that such a network can be trained end-to-end, which means the architecture of this network is very simple, straightforward and fast to train. Besides, for this project, the availability of the dataset is limited, which makes U-Net a more suitable choice. We also implemented the Faster R-CNN to achieve the object localization. Finally, we evaluated the performance of the two models and further compared the pros and cons of them. The preliminary results show that deep learning based technique outperforms all existing traditional segmentation algorithms.
Pinto, Tiago Alexandre Barbosa. "Object detection with artificial vision and neural networks for service robots." Master's thesis, 2018. http://hdl.handle.net/1822/62251.
Full textThis dissertation arises from a major project that consists on developing a domestic service robot, named CHARMIE (Collaborative Home Assistant Robot by Minho Industrial Electronics), to cooperate and help on domestic tasks. In general, the project aims to implement artificial intelligence in the whole robot. The main contribution of this dissertation is the development of the vision system, with artificial intelligence, to classify and detect, in real time, the objects represented on the environment that the robot is placed. This dissertation is within two broad areas that revolutionized the robotics industry, namely the artificial vision and artificial intelligence. Knowing that most of the existent information is presented on the vision and with the evolution of robotics, there was a need to introduce the capacity to acquire and process this kind of information. So, the artificial vision algorithms allowed them to acquire information of the environment, namely patterns, objects, formats, through vision sensors (cameras). Although implementing artificial vision can be very complex if it is intended to detect objects, due to image complexity. The introduction of artificial intelligence, more precisely, deep learning, brought the capability of implementing systems that can learn from provided data, without the need of hard coding it, reducing slightly the complexity and the time consumption of implementing complex problems. For artificial vision problems, like this project, there is a deep neural network that is specialized in learning from three dimensional vectors, namely images, named Convolutional Neural Network (CNN). This network uses image data to learn patterns, edges, formats, and many more, that represents a certain object. This type of network is used to classify and detect the objects presented in the image provided by the camera and is implemented with the Tensorflow library. All the image acquisition from the camera is performed by the OpenCv library. At the end of the dissertation, a model that allows real-time detection of objects from camera images is provided.
Albuquerque, Carina Isabel Andrade. "Convolutional neural networks for cell detection and counting : a case study of human cell quantification in zebrafish xenografts using deep learning object detection techniques." Master's thesis, 2019. http://hdl.handle.net/10362/62425.
Full textDeep learninghad,inrecentyears,becamethestateofthearttodealwithcomputer vision problems.Onthecomputervisionresearchfield,objectdetectionisatechnique thatallowstolocalizeandclassifyoneormoreobjectsinaninputimage.Thisapproach can beappliedtoseveraltasksandproblems,ascellcountinginmedicalimaging,as proposed inthisthesis. Cellcountingisafrequentlyneededtaskinseveralmedicaltypesofresearch,butof- ten stillmademanuallyduetoseveralconstraints.Theautomationofthisprocesscan be challengingtoachieve,especiallywhendealingwithcellclumpingandoverlapping, and cellsthatcanassumeseveralshapesandsizes.However,doingthisneededpro- cess manuallyturnsouttobeabottleneckconcerningspeedduringtheresearch.As so, anautomatictoolthatallowsresearcherstoquantifycellswithdifferentfeaturesin an accuratewayisalongdesiredapplicationinthemedicalcommunity.Inthisthesis, a fine-tunedarchitecturebasedonFasterR-CNNobjectdetectionalgorithmandIncep- tion ResnetV2featureextractorisproposedtodealwithcellquantificationinzebrafish xenografts,aninnovativeapproachforthestudyofcancer,metastasis,anddrugdiscov- ery,currentlybeingappliedatFundaçãoChampalimaud,worldwidereferenceinonco- logictreatmentinnovation. In thisway,itisshownthepracticalapplicationoftheproposedsolutiontoaddress a problemthatremainsinthecontextofmedicalresearchinFundaçãoChampalimaud, where ateamofresearchersexplorestheapproachofcellcountingandhistologicalanal- ysis inzebrafishlarvaexenotransplantstoevaluatetheresponseoftherapiesincancer. As so,thisthesisaimstobeacontributiontotheapplicationofobjectdetectiontech- niques tocellcountingtasks,andaddressseveralproblemsusuallyassociatedwiththis process, asthepresenceofoverlappedcells,thehighnumberofobjectstobedetected and theheterogeneityofcellsconcerningsizeandshape.
Nos últimos anos,Deep Learning revelou-se como a tecnologia de vanguarda para lidar com problemas de visão computacional. Incluído no campo de pesquisa de visão com- putacional, adeteçãodeobjetoséumatécnicaquepermitelocalizareclassificarumou mais objetosnumaimagemdeinput.Estaabordagempodeseraplicadanosmaisdiver- sos problemas,talcomocontagemdecélulasemimagensmédicas,comopropostonesta tese. A contagemdecélulaséumatarefafrequentementenecessáriaemdiversasáreasde pesquisa médica,mas maioritariamente ainda realizada manualmente devido a diversas limitações. A automatização deste processo pode ser desafiante de atingir,especialmente quando lidamoscomaglomeraçãoesobreposiçãodecélulas,esituaçõesemqueascélu- las podem assumir diversas formas e tamanhos. No entanto,fazer este processo de modo manual revela-se como uma fase de constrangimento temporal na investigação.Como tal, uma ferramenta automática que permite a investigadores a quantificção de células de diversas características de modo a cura do tornou-se uma aplicação fortemente dese- jada nacomunidademédica.Nestatese,umaarquiteturaajustadabaseadanoalgoritmo de deteçãodeobjetosFasterR-CNNenoInceptionResnetv2épropostademodoalidar com aquantificação de células em xenoenxertosdezebrafish,uma abordagem inovadora para o estudo de cancro,metástases e descoberta de drogas, e atualmente a ser desen- volvida na Fundação Champalimaud,uma referência mundialemtermosde inovação no tratamento oncológico. Desta maneira,éapresentadaaaplicaçãopráticadasoluçãopropostaparaendereçarum problema que sem antém no contexto de pesquisa médica na Fundação Champalimaud, onde umaequipadeinvestigaçãoexploraacontagemdecélulaseaanálisehistológica em xenotransplantesrealizadosemzebrafishlarvaeparaavaliararespostadeterapias em células cancerígenas.