Relevant bibliographies by topics / Deep Learning, Computer Vision, Object Detection / Dissertations / Theses
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Dissertations / Theses on the topic 'Deep Learning, Computer Vision, Object Detection'

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Published: 10 March 2023

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1

Kohmann, Erich. "Tecniche di deep learning per l'object detection." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19637/.

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L’object detection è uno dei principali problemi nell’ambito della computer vision. Negli ultimi anni, con l’avvento delle reti neurali e del deep learning, sono stati fatti notevoli progressi nei metodi per affrontare questo problema. Questa tesi intende fornire una rassegna dei principali modelli di object detection basati su deep learning, di cui si illustrano le caratteristiche fondamentali e gli elementi che li contraddistinguono dai modelli precedenti. Dopo un infarinatura iniziale sul deep learning e sulle reti neurali in genere, vengono presentati i modelli caratterizzati da tecniche
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Andersson, Dickfors Robin, and Nick Grannas. "OBJECT DETECTION USING DEEP LEARNING ON METAL CHIPS IN MANUFACTURING." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-55068.

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Designing cutting tools for the turning industry, providing optimal cutting parameters is of importance for both the client, and for the company's own research. By examining the metal chips that form in the turning process, operators can recommend optimal cutting parameters. Instead of doing manual classification of metal chips that come from the turning process, an automated approach of detecting chips and classification is preferred. This thesis aims to evaluate if such an approach is possible using either a Convolutional Neural Network (CNN) or a CNN feature extraction coupled with machine
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Arefiyan, Khalilabad Seyyed Mostafa. "Deep Learning Models for Context-Aware Object Detection." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/88387.

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In this thesis, we present ContextNet, a novel general object detection framework for incorporating context cues into a detection pipeline. Current deep learning methods for object detection exploit state-of-the-art image recognition networks for classifying the given region-of-interest (ROI) to predefined classes and regressing a bounding-box around it without using any information about the corresponding scene. ContextNet is based on an intuitive idea of having cues about the general scene (e.g., kitchen and library), and changes the priors about presence/absence of some object classes. We p
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4

Bartoli, Giacomo. "Edge AI: Deep Learning techniques for Computer Vision applied to embedded systems." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16820/.

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In the last decade, Machine Learning techniques have been used in different fields, ranging from finance to healthcare and even marketing. Amongst all these techniques, the ones adopting a Deep Learning approach were revealed to outperform humans in tasks such as object detection, image classification and speech recognition. This thesis introduces the concept of Edge AI: that is the possibility to build learning models capable of making inference locally, without any dependence on expensive servers or cloud services. A first case study we consider is based on the Google AIY Vision Kit, an inte
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Espis, Andrea. "Object detection and semantic segmentation for assisted data labeling." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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The automation of data labeling tasks is a solution to the errors and time costs related to human labeling. In this thesis work CenterNet, DeepLabV3, and K-Means applied to the RGB color space, are deployed to build a pipeline for Assisted data labeling: a semi-automatic process to iteratively improve the quality of the annotations. The proposed pipeline pointed out a total of 1547 wrong and missing annotations when applied to a dataset originally containing 8,300 annotations. Moreover, the quality of each annotation has been drastically improved, and at the same time, more than 600 hours of w
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Norrstig, 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.

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Visual object detection is a popular computer vision task that has been intensively investigated using deep learning on real data. However, data from virtual environments have not received the same attention. A virtual environment enables generating data for locations that are not easily reachable for data collection, e.g. aerial environments. In this thesis, we study the problem of object detection in virtual environments, more specifically an aerial virtual environment. We use a simulator, to generate a synthetic data set of 16 different types of vehicles captured from an airplane. To study
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Dickens, 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.

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The rise of convolutional neural networks (CNNs) in the context of computer vision has occurred in tandem with the advancement of depth sensing technology. Depth cameras are capable of yielding two-dimensional arrays storing at each pixel the distance from objects and surfaces in a scene from a given sensor, aligned with a regular color image, obtaining so-called RGBD images. Inspired by prior models in the literature, this work develops a suite of RGBD CNN models to tackle the challenging tasks of object detection, instance segmentation, and semantic segmentation. Prominent architectur
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8

Solini, Arianna. "Applicazione di Deep Learning e Computer Vision ad un Caso d'uso aziendale: Progettazione, Risoluzione ed Analisi." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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Nella computer vision, sono oramai più di dieci anni che si parla di Machine Learning (ML), con l'obiettivo di creare sistemi autonomi che siano in grado di realizzare modelli approssimati della realtà tridimensionale partendo da immagini bidimensionali. Grazie a questa capacità si possono interpretare e comprendere le immagini, emulando la vista umana. Molti ricercatori hanno creato reti neurali in grado di sfidarsi su grandi dataset di milioni di immagini e, come conseguenza, si è ottenuto il continuo miglioramento delle performance di classificazione di immagini da parte delle reti e la cap
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9

Cuan, Bonan. "Deep similarity metric learning for multiple object tracking." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI065.

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Le suivi d’objets multiples dans une scène est une tâche importante dans le domaine de la vision par ordinateur, et présente toujours de très nombreux verrous. Les objets doivent être détectés et distingués les uns des autres de manière continue et simultanée. Les approches «suivi par détection» sont largement utilisées, où la détection des objets est d’abord réalisée sur toutes les frames, puis le suivi est ramené à un problème d’association entre les détections d’un même objet et les trajectoires identifiées. La plupart des algorithmes de suivi associent des modèles de mouvement et des modèl
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10

Chen, Zhe. "Augmented Context Modelling Neural Networks." Thesis, The University of Sydney, 2019. http://hdl.handle.net/2123/20654.

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Contexts provide beneficial information for machine-based image understanding tasks. However, existing context modelling methods still cannot fully exploit contexts, especially for object recognition and detection. In this thesis, we develop augmented context modelling neural networks to better utilize contexts for different object recognition and detection tasks. Our contributions are two-fold: 1) we introduce neural networks to better model instance-level visual relationships; 2) we introduce neural network-based algorithms to better utilize contexts from 3D information and synthesized data
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Gustafsson, Fredrik, and Erik Linder-Norén. "Automotive 3D Object Detection Without Target Domain Annotations." Thesis, Linköpings universitet, Datorseende, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148585.

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In this thesis we study a perception problem in the context of autonomous driving. Specifically, we study the computer vision problem of 3D object detection, in which objects should be detected from various sensor data and their position in the 3D world should be estimated. We also study the application of Generative Adversarial Networks in domain adaptation techniques, aiming to improve the 3D object detection model's ability to transfer between different domains. The state-of-the-art Frustum-PointNet architecture for LiDAR-based 3D object detection was implemented and found to closely match
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Ogier, du Terrail Jean. "Réseaux de neurones convolutionnels profonds pour la détection de petits véhicules en imagerie aérienne." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMC276/document.

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Cette thèse présente une tentative d'approche du problème de la détection et discrimination des petits véhicules dans des images aériennes en vue verticale par l'utilisation de techniques issues de l'apprentissage profond ou "deep-learning". Le caractère spécifique du problème permet d'utiliser des techniques originales mettant à profit les invariances des automobiles et autres avions vus du ciel.Nous commencerons par une étude systématique des détecteurs dits "single-shot", pour ensuite analyser l'apport des systèmes à plusieurs étages de décision sur les performances de détection. Enfin nous
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13

Taurone, Francesco. "3D Object Recognition from a Single Image via Patch Detection by a Deep CNN." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18669/.

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This thesis describes the development of a new technique for recognizing the 3D pose of an object via a single image. The whole project is based on a CNN for recognizing patches on the object, that we use for estimating the pose given an a priori model. The positions of the patches, together with the knowledge of their coordinates in the model, make the estimation of the pose possible through a solution of a PnP problem. The CNN chosen for this project is Yolo. In order to build the training dataset for the network, a new approach is used. Instead of labeling each individual training image
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Al, Hakim Ezeddin. "3D YOLO: End-to-End 3D Object Detection Using Point Clouds." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234242.

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For safe and reliable driving, it is essential that an autonomous vehicle can accurately perceive the surrounding environment. Modern sensor technologies used for perception, such as LiDAR and RADAR, deliver a large set of 3D measurement points known as a point cloud. There is a huge need to interpret the point cloud data to detect other road users, such as vehicles and pedestrians. Many research studies have proposed image-based models for 2D object detection. This thesis takes it a step further and aims to develop a LiDAR-based 3D object detection model that operates in real-time, with empha
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Fucili, Mattia. "3D object detection from point clouds with dense pose voters." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17616/.

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Il riconoscimento di oggetti è sempre stato un compito sfidante per la Computer Vision. Trova applicazione in molti campi, principalmente nell’industria, come ad esempio per permettere ad un robot di trovare gli oggetti da afferrare. Negli ultimi decenni tali compiti hanno trovato nuovi modi di essere raggiunti grazie alla riscoperta delle Reti Neurali, in particolare le Reti Neurali Convoluzionali. Questo tipo di reti ha raggiunto ottimi risultati in molte applicazioni per il riconoscimento e la classificazione degli oggetti. La tendenza, ora, `e quella di utilizzare tali reti anche nell’indust
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Capuzzo, Davide. "3D StixelNet Deep Neural Network for 3D object detection stixel-based." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/22017/.

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In this thesis it has been presented an algorithm of deep learning for 3D object detection from the point cloud in an outdoor environment. This algorithm is feed with stixel, a medium-type data generates starting from a point cloud or depth map. A stixel can be think as a small rectangle that start form the base of the road and then rises until the top of the obstacle summarizing the vertical surface of an object. The goal of stixel is to compress the data coming from sensors in order to have a fast transmission without losing information. The algorithm to generate stixel is a novel algorithm
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Azizpour, Hossein. "Visual Representations and Models: From Latent SVM to Deep Learning." Doctoral thesis, KTH, Datorseende och robotik, CVAP, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192289.

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Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. This thesis, in its general form, proposes different techniques within the frameworks of two learning systems for representation and modeling. Namely, latent support vector machines (latent SVMs) and deep learning. First, we propose various approaches to group the positive samples into clusters of visually similar instances. Given a fixed representation, the sample
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18

Kalogeiton, Vasiliki. "Localizing spatially and temporally objects and actions in videos." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28984.

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The rise of deep learning has facilitated remarkable progress in video understanding. This thesis addresses three important tasks of video understanding: video object detection, joint object and action detection, and spatio-temporal action localization. Object class detection is one of the most important challenges in computer vision. Object detectors are usually trained on bounding-boxes from still images. Recently, video has been used as an alternative source of data. Yet, training an object detector on one domain (either still images or videos) and testing on the other one results in a sign
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19

Söderlund, Henrik. "Real-time Detection and Tracking of Moving Objects Using Deep Learning and Multi-threaded Kalman Filtering : A joint solution of 3D object detection and tracking for Autonomous Driving." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160180.

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Perception for autonomous drive systems is the most essential function for safe and reliable driving. LiDAR sensors can be used for perception and are vying for being crowned as an essential element in this task. In this thesis, we present a novel real-time solution for detection and tracking of moving objects which utilizes deep learning based 3D object detection. Moreover, we present a joint solution which utilizes the predictability of Kalman Filters to infer object properties and semantics to the object detection algorithm, resulting in a closed loop of object detection and object tracking
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20

Peng, Zeng. "Pedestrian Tracking by using Deep Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-302107.

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This project aims at using deep learning to solve the pedestrian tracking problem for Autonomous driving usage. The research area is in the domain of computer vision and deep learning. Multi-Object Tracking (MOT) aims at tracking multiple targets simultaneously in a video data. The main application scenarios of MOT are security monitoring and autonomous driving. In these scenarios, we often need to track many targets at the same time which is not possible with only object detection or single object tracking algorithms for their lack of stability and usability. Therefore we need to explore the
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21

Papakis, Ioannis. "A Graph Convolutional Neural Network Based Approach for Object Tracking Using Augmented Detections With Optical Flow." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103372.

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This thesis presents a novel method for online Multi-Object Tracking (MOT) using Graph Convolutional Neural Network (GCNN) based feature extraction and end-to-end feature matching for object association. The Graph based approach incorporates both appearance and geometry of objects at past frames as well as the current frame into the task of feature learning. This new paradigm enables the network to leverage the "contextual" information of the geometry of objects and allows us to model the interactions among the features of multiple objects. Another central innovation of the proposed framework
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Mhalla, Ala. "Multi-object detection and tracking in video sequences." Thesis, Université Clermont Auvergne‎ (2017-2020), 2018. http://www.theses.fr/2018CLFAC084/document.

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Le travail développé dans cette thèse porte sur l'analyse de séquences vidéo. Cette dernière est basée sur 3 taches principales : la détection, la catégorisation et le suivi des objets. Le développement de solutions fiables pour l'analyse de séquences vidéo ouvre de nouveaux horizons pour plusieurs applications telles que les systèmes de transport intelligents, la vidéosurveillance et la robotique. Dans cette thèse, nous avons mis en avant plusieurs contributions pour traiter les problèmes de détection et de suivi d'objets multiples sur des séquences vidéo. Les techniques proposées sont basées
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Grossman, Mikael. "Proposal networks in object detection." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-241918.

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Locating and extracting useful data from images is a task that has been revolutionized in the last decade as computing power has risen to such a level to use deep neural networks with success. A type of neural network that uses the convolutional operation called convolutional neural network (CNN) is suited for image related tasks. Using the convolution operation creates opportunities for the network to learn their own filters, that previously had to be hand engineered. For locating objects in an image the state-of-the-art Faster R-CNN model predicts objects in two parts. Firstly, the region pro
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Moussallik, Laila. "Towards Condition-Based Maintenance of Catenary wires using computer vision : Deep Learning applications on eMaintenance & Industrial AI for railway industry." Thesis, Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-83123.

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Railways are a main element of a sustainable transport policy in several countries as they are considered a safe, efficient and green mode of transportation. Owing to these advantages, there is a cumulative request for the railway industry to increase the performance, the capacity and the availability in addition to safely transport goods and people at higher speeds. To meet the demand, large adjustment of the infrastructure and improvement of maintenance process are required.  Inspection activities are essential in establishing the required maintenance, and it is periodically required to redu
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IACONO, MASSIMILIANO. "Object detection and recognition with event driven cameras." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1005981.

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This thesis presents study, analysis and implementation of algorithms to perform object detection and recognition using an event-based cam era. This sensor represents a novel paradigm which opens a wide range of possibilities for future developments of computer vision. In partic ular it allows to produce a fast, compressed, illumination invariant output, which can be exploited for robotic tasks, where fast dynamics and significant illumination changes are frequent. The experiments are carried out on the neuromorphic version of the iCub humanoid platform. The robot is equipped with a nov
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Lamberti, Lorenzo. "A deep learning solution for industrial OCR applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19777/.

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This thesis describes a project developed throughout a six months internship in the Machine Vision Laboratory of Datalogic based in Pasadena, California. The project aims to develop a deep learning system as a possible solution for industrial optical character recognition applications. In particular, the focus falls on a specific algorithm called You Only Look Once (YOLO), which is a general-purpose object detector based on convolutional neural networks that currently offers state-of-the-art performances in terms of trade-off between speed and accuracy. This algorithm is indeed well known fo
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Cottignoli, Lorenzo. "Strumento di Realtà Aumentata su Dispositivi Mobili per Labeling di Immagini Semi-Automatico." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17734/.

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In questa tesi verrà proposta l'implementazione di un sistema innovativo di realtà aumentata su dispositivi mobili per il labeling semi-automatico di immagini. L'obiettivo di questa tesi è quello di fornire uno strumento di supporto semplice ed intuitivo, in grado di ridurre drasticamente i tempi di generazione di dataset, necessari per l'addestramento delle reti neurali per l'Object Detection. Per questo motivo, è stata sviluppata un'applicazione mobile per Android che permetta la creazione di dataset in maniera semi-automatica. Inoltre, è stato realizzato uno script Python per desktop in g
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Arcidiacono, Claudio Salvatore. "An empirical study on synthetic image generation techniques for object detectors." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-235502.

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Convolutional Neural Networks are a very powerful machine learning tool that outperformed other techniques in image recognition tasks. The biggest drawback of this method is the massive amount of training data required, since producing training data for image recognition tasks is very labor intensive. To tackle this issue, different techniques have been proposed to generate synthetic training data automatically. These synthetic data generation techniques can be grouped in two categories: the first category generates synthetic images using computer graphic software and CAD models of the objects
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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.

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Vision based active safety systems have become more frequently occurring in modern vehicles to estimate depth of the objects ahead and for autonomous driving (AD) and advanced driver-assistance systems (ADAS). In this thesis a lightweight deep convolutional neural network performing real-time depth estimation on single monocular images is implemented and evaluated. Many of the vision based automatic brake systems in modern vehicles only detect pre-trained object types such as pedestrians and vehicles. These systems fail to detect general objects such as road debris and roadside obstacles. In s
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Pitteri, Giorgia. "3D Object Pose Estimation in Industrial Context." Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0202.

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La détection d'objets 3D et l'estimation de leur pose à partir d'images sont très importantes pour des tâches comme la robotique et la réalité augmentée et font l'objet d'intenses recherches depuis le début de la vision par ordinateur. D'importants progrès ont été réalisés récemment grâce au développement des méthodes basées sur l'apprentissage profond. Ce type d'approche fait néanmoins face à plusieurs obstacles majeurs qui se révèlent en milieu industriel, notamment la gestion des objets contenant des symétries et la généralisation à de nouveaux objets jamais vus par les réseaux lors de l'ap
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Grard, Matthieu. "Generic instance segmentation for object-oriented bin-picking." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEC015.

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Le dévracage robotisé est une tâche industrielle en forte croissance visant à automatiser le déchargement par unité d’une pile d’instances d'objet en vrac pour faciliter des traitements ultérieurs tels que la formation de kits ou l’assemblage de composants. Cependant, le modèle explicite des objets est souvent indisponible dans de nombreux secteurs industriels, notamment alimentaire et automobile, et les instances d'objet peuvent présenter des variations intra-classe, par exemple en raison de déformations élastiques.Les techniques d’estimation de pose, qui nécessitent un modèle explicite et su
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Rispoli, Luca. "Un approccio deep learning-based per il conteggio di persone tramite videocamere low-cost in un contesto Smart Campus." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19567/.

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I più recenti progressi tecnologici hanno provocato una rapida evoluzione del settore delle tecnologie cosiddette Smart, che, ad oggi, vengono integrati in un vasto numero di sistemi. La diffusione di tali tecnologie non si è tuttavia limitata a dispositivi ed apparecchiature informatiche, ma ha coinvolto anche altri settori, come quello edilizio, la quale influenza ha dato vita al concetto di "smart building". Un edificio intelligente ha lo scopo di offrire ai suoi abitanti un elevato livello di comfort, creando un ecosistema in cui i vari dispositivi elettronici possono operare interagendo
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Suzano, Massa Francisco Vitor. "Mise en relation d'images et de modèles 3D avec des réseaux de neurones convolutifs." Thesis, Paris Est, 2017. http://www.theses.fr/2017PESC1198/document.

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La récente mise à disposition de grandes bases de données de modèles 3D permet de nouvelles possibilités pour un raisonnement à un niveau 3D sur les photographies. Cette thèse étudie l'utilisation des réseaux de neurones convolutifs (CNN) pour mettre en relation les modèles 3D et les images.Nous présentons tout d'abord deux contributions qui sont utilisées tout au long de cette thèse : une bibliothèque pour la réduction automatique de la mémoire pour les CNN profonds, et une étude des représentations internes apprises par les CNN pour la mise en correspondance d'images appartenant à des domain
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Carletti, Angelo. "Development of a machine learning algorithm for the automatic analysis of microscopy images in an in-vitro diagnostic platform." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In this thesis we present the development of machine learning algorithms for single cell analysis in an in-vitro diagnostic platform for Cellply, a startup that operates in precision medicine. We researched the state of the art of deep learning for biomedical image analysis, and we analyzed the impact that convolutional neural networks have had in object detection tasks. Then we compared neural networks that are currently used for cell detection, and we chose the one (i.e. Stardist) that is able to perform a more efficient detection also in a crowded cells context. We could train models usi
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Sievert, Rolf. "Instance Segmentation of Multiclass Litter and Imbalanced Dataset Handling : A Deep Learning Model Comparison." Thesis, Linköpings universitet, Datorseende, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-175173.

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Instance segmentation has a great potential for improving the current state of littering by autonomously detecting and segmenting different categories of litter. With this information, litter could, for example, be geotagged to aid litter pickers or to give precise locational information to unmanned vehicles for autonomous litter collection. Land-based litter instance segmentation is a relatively unexplored field, and this study aims to give a comparison of the instance segmentation models Mask R-CNN and DetectoRS using the multiclass litter dataset called Trash Annotations in Context (TACO) i
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Gao, Yuan. "Surround Vision Object Detection Using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231929.

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The thesis first develops an object detection framework for front view camera images in surround vision data set. And with the goal of reducing as much annotated data as possible, various domain adaptation methods are applied to train other camera images based on the pretraining of a baseline model. Relevant data analysis work is performed to reveal useful information in object distribution over all cameras. Regularization techniques involving dropout, weight decay, data augmentation are attempted to lower the complexity of training model. Also, the experiments of ratio reduction are carried o
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Mordan, Taylor. "Conception d'architectures profondes pour l'interprétation de données visuelles." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS270.

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Aujourd’hui, les images sont omniprésentes à travers les smartphones et les réseaux sociaux. Il devient alors nécessaire d’avoir des moyens de traitement automatiques, afin d’analyser et d’interpréter les grandes quantités de données disponibles. Dans cette thèse, nous nous intéressons à la détection d’objets, i.e. au problème d’identification et de localisation de tous les objets présents dans une image. Cela peut être vu comme une première étape vers une interprétation complète des scènes. Nous l’abordons avec des réseaux de neurones profonds à convolutions, sous le paradigme de l’apprentiss
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Zhang, Kaige. "Deep Learning for Crack-Like Object Detection." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7616.

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Cracks are common defects on surfaces of man-made structures such as pavements, bridges, walls of nuclear power plants, ceilings of tunnels, etc. Timely discovering and repairing of the cracks are of great significance and importance for keeping healthy infrastructures and preventing further damages. Traditionally, the cracking inspection was conducted manually which was labor-intensive, time-consuming and costly. For example, statistics from the Central Intelligence Agency show that the world’s road network length has reached 64,285,009 km, of which the United States has 6,586,610 km. It is a
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Yellapantula, Sudha Ravali. "Synthesizing Realistic Data for Vision Based Drone-to-Drone Detection." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/91460.

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In the thesis, we aimed at building a robust UAV(drone) detection algorithm through which, one drone could detect another drone in flight. Though this was a straight forward object detection problem, the biggest challenge we faced for drone detection is the limited amount of drone images for training. To address this issue, we used Generative Adversarial Networks, CycleGAN to be precise, for the generation of realistic looking fake images which were indistinguishable from real data. CycleGAN is a classic example of Image to Image Translation technique, and we this applied in our situation wher
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Matosevic, Antonio. "Batch Active Learning for Deep Object Detection in Videos." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-292035.

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Relatively recent progress in object detection can mainly be attributed to the success of deep neural networks. However, training such models requires large amounts of annotated data. This poses a two-fold problem, namely obtaining labelled data is a time-consuming process, and training models on many instances is computationally costly. To this end a common approach is to employ active learning, which amounts to constructing a strategy to interactively query much fewer data points while maximizing the performance. In the context of deep object detection in videos, two new challenges arise. Fi
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Ibrahim, Ahmed Sobhy Elnady. "End-To-End Text Detection Using Deep Learning." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/81277.

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Text detection in the wild is the problem of locating text in images of everyday scenes. It is a challenging problem due to the complexity of everyday scenes. This problem possesses a great importance for many trending applications, such as self-driving cars. Previous research in text detection has been dominated by multi-stage sequential approaches which suffer from many limitations including error propagation from one stage to the next. Another line of work is the use of deep learning techniques. Some of the deep methods used for text detection are box detection models and fully convolutio
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42

Rahman, Quazi Marufur. "Performance monitoring of deep learning vision systems during deployment." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/229733/1/Quazi%20Marufur_Rahman_Thesis.pdf.

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This thesis investigates how to monitor the performance of deep learning vision systems in mobile robots. It conducts state-of-the-art research to validate the real-time performance of mobile robots such as self-driving cars. This research is significant for deploying visual sensor-dependent autonomous vehicles in our daily lives. This knowledge will alert a mobile robot about its performance degradation to take preventive measures to reduce the risk of hazardous consequences for the robot, its surroundings and any person involved.
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Capellier, Édouard. "Application of machine learning techniques for evidential 3D perception, in the context of autonomous driving." Thesis, Compiègne, 2020. http://www.theses.fr/2020COMP2534.

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L’apprentissage machine a révolutionné la manière dont les problèmes de perception sont, actuellement, traités. En effet, la plupart des approches à l’état de l’art, dans de nombreux domaines de la vision par ordinateur, se reposent sur des réseaux de neurones profonds. Au moment de déployer, d’évaluer, et de fusionner de telles approches au sein de véhicules autonomes, la question de la représentation des connaissances extraites par ces approches se pose. Dans le cadre de ces travaux de thèse, effectués au sein de Renault SAS, nous avons supposé qu’une représentation crédibiliste permettait d
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Aytar, Yusuf. "Transfer learning for object category detection." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:c9e18ff9-df43-4f67-b8ac-28c3fdfa584b.

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Object category detection, the task of determining if one or more instances of a category are present in an image with their corresponding locations, is one of the fundamental problems of computer vision. The task is very challenging because of the large variations in imaged object appearance, particularly due to the changes in viewpoint, illumination and intra-class variance. Although successful solutions exist for learning object category detectors, they require massive amounts of training data. Transfer learning builds upon previously acquired knowledge and thus reduces training requirement
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Moniruzzaman, Md. "Seagrass detection using deep learning." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2019. https://ro.ecu.edu.au/theses/2261.

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Seagrasses play an essential role in the marine ecosystem by providing foods, nutrients, and habitat to the marine lives. They work as marine bioindicators by reflecting the health condition of aquatic environments. Seagrasses also act as a significant atmospheric carbon sink that mitigates global warming and rapid climate changes. Considering the importance, it is critical to monitor seagrasses across the coastlines which includes detection, mapping, percentage cover calculation, and health estimation. Remote sensing-based aerial and spectral images, acoustic images, underwater two-dimensiona
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Runow, Björn. "Deep Learning for Point Detection in Images." Thesis, Linköpings universitet, Datorseende, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-166644.

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The main result of this thesis is a deep learning model named BearNet, which can be trained to detect an arbitrary amount of objects as a set of points. The model is trained using the Weighted Hausdorff distance as loss function. BearNet has been applied and tested on two problems from the industry. These are: From an intensity image, detect two pocket points of an EU-pallet which an autonomous forklift could utilize when determining where to insert its forks. From a depth image, detect the start, bend and end points of a straw attached to a juice package, in order to help determine if the str
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Öhman, Wilhelm. "Data augmentation using military simulators in deep learning object detection applications." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264917.

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While deep learning solutions have made great progress in recent years, the requirement of large labeled datasets still limit their practical use in certain areas. This problem is especially acute for solutions in domains where even unlabeled data is a limited resource, such as the military domain. Synthetic data, or artificially generated data, has recently attracted attention as a potential solution for this problem. This thesis explores the possibility of using synthetic data in order to improve the performance of a neural network aimed at detecting and localizing firearms in images. To gen
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Estgren, Martin. "Bone Fragment Segmentation Using Deep Interactive Object Selection." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157668.

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In recent years semantic segmentation models utilizing Convolutional Neural Networks (CNN) have seen significant success for multiple different segmentation problems. Models such as U-Net have produced promising results within the medical field for both regular 2D and volumetric imaging, rivalling some of the best classical segmentation methods. In this thesis we examined the possibility of using a convolutional neural network-based model to perform segmentation of discrete bone fragments in CT-volumes with segmentation-hints provided by a user. We additionally examined different classical seg
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Case, Isaac. "Automatic object detection and tracking in video /." Online version of thesis, 2010. http://hdl.handle.net/1850/12332.

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Cogswell, Michael Andrew. "Understanding Representations and Reducing their Redundancy in Deep Networks." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/78167.

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Neural networks in their modern deep learning incarnation have achieved state of the art performance on a wide variety of tasks and domains. A core intuition behind these methods is that they learn layers of features which interpolate between two domains in a series of related parts. The first part of this thesis introduces the building blocks of neural networks for computer vision. It starts with linear models then proceeds to deep multilayer perceptrons and convolutional neural networks, presenting the core details of each. However, the introduction also focuses on intuition by visualizing c
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