434 research outputs found

    Deep neural networks for marine debris detection in sonar images

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    Garbage and waste disposal is one of the biggest challenges currently faced by mankind. Proper waste disposal and recycling is a must in any sustainable community, and in many coastal areas there is significant water pollution in the form of floating or submerged garbage. This is called marine debris. It is estimated that 6.4 million tonnes of marine debris enter water environments every year [McIlgorm et al. 2008, APEC Marine Resource Conservation WG], with 8 million items entering each day. An unknown fraction of this sinks to the bottom of water bodies. Submerged marine debris threatens marine life, and for shallow coastal areas, it can also threaten fishing vessels [Iñiguez et al. 2016, Renewable and Sustainable Energy Reviews]. Submerged marine debris typically stays in the environment for a long time (20+ years), and consists of materials that can be recycled, such as metals, plastics, glass, etc. Many of these items should not be disposed in water bodies as this has a negative effect in the environment and human health. Encouraged by the advances in Computer Vision from the use Deep Learning, we propose the use of Deep Neural Networks (DNNs) to survey and detect marine debris in the bottom of water bodies (seafloor, lake and river beds) from Forward-Looking Sonar (FLS) images. This thesis performs a comprehensive evaluation on the use of DNNs for the problem of marine debris detection in FLS images, as well as related problems such as image classification, matching, and detection proposals. We do this in a dataset of 2069 FLS images that we captured with an ARIS Explorer 3000 sensor on marine debris objects lying in the floor of a small water tank. We had issues with the sensor in a real world underwater environment that motivated the use of a water tank. The objects we used to produce this dataset contain typical household marine debris and distractor marine objects (tires, hooks, valves, etc), divided in 10 classes plus a background class. Our results show that for the evaluated tasks, DNNs area superior technique than the corresponding state of the art. There are large gains particularly for the matching and detection proposal tasks. We also study the effect of sample complexity and object size in many tasks, which is valuable information for practitioners. We expect that our results will advance the objective of using Autonomous Underwater Vehicles to automatically survey, detect and collect marine debris from underwater environments

    Automatic target recognition in sonar imagery using a cascade of boosted classifiers

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    This thesis is concerned with the problem of automating the interpretation of data representing the underwater environment retrieved from sensors. This is an important task which potentially allows underwater robots to become completely autonomous, keeping humans out of harm’s way and reducing the operational time and cost of many underwater applications. Typical applications include unexploded ordnance clearance, ship/plane wreck hunting (e.g. Malaysia Airlines flight MH370), and oilfield inspection (e.g. Deepwater Horizon disaster). Two attributes of the processing are crucial if automated interpretation is to be successful. First, computational efficiency is required to allow real-time analysis to be performed on-board robots with limited resources. Second, detection accuracy comparable to human experts is required in order to replace them. Approaches in the open literature do not appear capable of achieving these requirements and this therefore has become the objective of this thesis. This thesis proposes a novel approach capable of recognizing targets in sonar data extremely rapidly with a low number of false alarms. The approach was originally developed for face detection in video, and it is applied to sonar data here for the first time. Aside from the application, the main contribution of this thesis, therefore, is in the way this approach is extended to reduce its training time and improve its detection accuracy. Results obtained on large sets of real sonar data on a variety of challenging terrains are presented to show the discriminative power of the proposed approach. In real field trials, the proposed approach was capable of processing sonar data real-time on-board underwater robots. In direct comparison with human experts, the proposed approach offers 40% reduction in the number of false alarms

    Scalable Hierarchical Gaussian Process Models for Regression and Pattern Classification

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    Gaussian processes, which are distributions over functions, are powerful nonparametric tools for the two major machine learning tasks: regression and classification. Both tasks are concerned with learning input-output mappings from example input-output pairs. In Gaussian process (GP) regression and classification, such mappings are modeled by Gaussian processes. In GP regression, the likelihood is Gaussian for continuous outputs, and hence closed-form solutions for prediction and model selection can be obtained. In GP classification, the likelihood is non-Gaussian for discrete/categorical outputs, and hence closed-form solutions are not available, and approximate inference methods must be resorted

    Comparing Learned Representations between Unpruned and Pruned Deep Convolutional Neural Networks

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    While deep neural networks have shown impressive performance in computer vision tasks, natural language processing, and other domains, the sizes and inference times of these models can often prevent them from being used on resource-constrained systems. Furthermore, as these networks grow larger in size and complexity, it can become even harder to understand the learned representations of the input data that these networks form through training. These issues of growing network size, increasing complexity and runtime, and ambiguity in the understanding of internal representations serve as guiding points for this work. In this thesis, we create a neural network that is capable of predicting up to three path waypoints given an input image. This network will be used in conjunction with other networks to help guide an autonomous robotic vehicle. Since this neural network will be deployed to an embedded system, it is important that our network is efficient. As such, we use a network compression technique known as L1 norm pruning to reduce the size of the network and speed up the inference time, while retaining similar loss. Furthermore, we investigate the effects that pruning has on the internal learned representations of models by comparing unpruned and pruned network layers using projection weighted canonical correlation analysis (PWCCA). Our results show that for deep convolutional neural networks (CNN), PWCCA similarity scores between early convolutional layers start low and then gradually increase towards the final layers of the network, with some peaks in the intermediate layers. We also show that for our deep CNN, linear layers at the end of the network also exhibit very high similarity, serving to guide the dissimilar representations from intermediate convolutional layers to a common representation that yields similar network performance between unpruned and pruned networks

    Learning cognitive maps: Finding useful structure in an uncertain world

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    In this chapter we will describe the central mechanisms that influence how people learn about large-scale space. We will focus particularly on how these mechanisms enable people to effectively cope with both the uncertainty inherent in a constantly changing world and also with the high information content of natural environments. The major lessons are that humans get by with a less is more approach to building structure, and that they are able to quickly adapt to environmental changes thanks to a range of general purpose mechanisms. By looking at abstract principles, instead of concrete implementation details, it is shown that the study of human learning can provide valuable lessons for robotics. Finally, these issues are discussed in the context of an implementation on a mobile robot. © 2007 Springer-Verlag Berlin Heidelberg

    Learning obstacle avoidance by a mobile robot

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    UAV-Enabled Surface and Subsurface Characterization for Post-Earthquake Geotechnical Reconnaissance

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    Major earthquakes continue to cause significant damage to infrastructure systems and the loss of life (e.g. 2016 Kaikoura, New Zealand; 2016 Muisne, Ecuador; 2015 Gorkha, Nepal). Following an earthquake, costly human-led reconnaissance studies are conducted to document structural or geotechnical damage and to collect perishable field data. Such efforts are faced with many daunting challenges including safety, resource limitations, and inaccessibility of sites. Unmanned Aerial Vehicles (UAV) represent a transformative tool for mitigating the effects of these challenges and generating spatially distributed and overall higher quality data compared to current manual approaches. UAVs enable multi-sensor data collection and offer a computational decision-making platform that could significantly influence post-earthquake reconnaissance approaches. As demonstrated in this research, UAVs can be used to document earthquake-affected geosystems by creating 3D geometric models of target sites, generate 2D and 3D imagery outputs to perform geomechanical assessments of exposed rock masses, and characterize subsurface field conditions using techniques such as in situ seismic surface wave testing. UAV-camera systems were used to collect images of geotechnical sites to model their 3D geometry using Structure-from-Motion (SfM). Key examples of lessons learned from applying UAV-based SfM to reconnaissance of earthquake-affected sites are presented. The results of 3D modeling and the input imagery were used to assess the mechanical properties of landslides and rock masses. An automatic and semi-automatic 2D fracture detection method was developed and integrated with a 3D, SfM, imaging framework. A UAV was then integrated with seismic surface wave testing to estimate the shear wave velocity of the subsurface materials, which is a critical input parameter in seismic response of geosystems. The UAV was outfitted with a payload release system to autonomously deliver an impulsive seismic source to the ground surface for multichannel analysis of surface waves (MASW) tests. The UAV was found to offer a mobile but higher-energy source than conventional seismic surface wave techniques and is the foundational component for developing the framework for fully-autonomous in situ shear wave velocity profiling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145793/1/wwgreen_1.pd

    Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

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    INE/AUTC 10.0

    An Orientation & Mobility Aid for People with Visual Impairments

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    Orientierung&Mobilität (O&M) umfasst eine Reihe von Techniken für Menschen mit Sehschädigungen, die ihnen helfen, sich im Alltag zurechtzufinden. Dennoch benötigen sie einen umfangreichen und sehr aufwendigen Einzelunterricht mit O&M Lehrern, um diese Techniken in ihre täglichen Abläufe zu integrieren. Während einige dieser Techniken assistive Technologien benutzen, wie zum Beispiel den Blinden-Langstock, Points of Interest Datenbanken oder ein Kompass gestütztes Orientierungssystem, existiert eine unscheinbare Kommunikationslücke zwischen verfügbaren Hilfsmitteln und Navigationssystemen. In den letzten Jahren sind mobile Rechensysteme, insbesondere Smartphones, allgegenwärtig geworden. Dies eröffnet modernen Techniken des maschinellen Sehens die Möglichkeit, den menschlichen Sehsinn bei Problemen im Alltag zu unterstützen, die durch ein nicht barrierefreies Design entstanden sind. Dennoch muss mit besonderer Sorgfalt vorgegangen werden, um dabei nicht mit den speziellen persönlichen Kompetenzen und antrainierten Verhaltensweisen zu kollidieren, oder schlimmstenfalls O&M Techniken sogar zu widersprechen. In dieser Dissertation identifizieren wir eine räumliche und systembedingte Lücke zwischen Orientierungshilfen und Navigationssystemen für Menschen mit Sehschädigung. Die räumliche Lücke existiert hauptsächlich, da assistive Orientierungshilfen, wie zum Beispiel der Blinden-Langstock, nur dabei helfen können, die Umgebung in einem limitierten Bereich wahrzunehmen, während Navigationsinformationen nur sehr weitläufig gehalten sind. Zusätzlich entsteht diese Lücke auch systembedingt zwischen diesen beiden Komponenten — der Blinden-Langstock kennt die Route nicht, während ein Navigationssystem nahegelegene Hindernisse oder O&M Techniken nicht weiter betrachtet. Daher schlagen wir verschiedene Ansätze zum Schließen dieser Lücke vor, um die Verbindung und Kommunikation zwischen Orientierungshilfen und Navigationsinformationen zu verbessern und betrachten das Problem dabei aus beiden Richtungen. Um nützliche relevante Informationen bereitzustellen, identifizieren wir zuerst die bedeutendsten Anforderungen an assistive Systeme und erstellen einige Schlüsselkonzepte, die wir bei unseren Algorithmen und Prototypen beachten. Existierende assistive Systeme zur Orientierung basieren hauptsächlich auf globalen Navigationssatellitensystemen. Wir versuchen, diese zu verbessern, indem wir einen auf Leitlinien basierenden Routing Algorithmus erstellen, der auf individuelle Bedürfnisse anpassbar ist und diese berücksichtigt. Generierte Routen sind zwar unmerklich länger, aber auch viel sicherer, gemäß den in Zusammenarbeit mit O&M Lehrern erstellten objektiven Kriterien. Außerdem verbessern wir die Verfügbarkeit von relevanten georeferenzierten Datenbanken, die für ein derartiges bedarfsgerechtes Routing benötigt werden. Zu diesem Zweck erstellen wir einen maschinellen Lernansatz, mit dem wir Zebrastreifen in Luftbildern erkennen, was auch über Ländergrenzen hinweg funktioniert, und verbessern dabei den Stand der Technik. Um den Nutzen von Mobilitätsassistenz durch maschinelles Sehen zu optimieren, erstellen wir O&M Techniken nachempfundene Ansätze, um die räumliche Wahrnehmung der unmittelbaren Umgebung zu erhöhen. Zuerst betrachten wir dazu die verfügbare Freifläche und informieren auch über mögliche Hindernisse. Weiterhin erstellen wir einen neuartigen Ansatz, um die verfügbaren Leitlinien zu erkennen und genau zu lokalisieren, und erzeugen virtuelle Leitlinien, welche Unterbrechungen überbrücken und bereits frühzeitig Informationen über die nächste Leitlinie bereitstellen. Abschließend verbessern wir die Zugänglichkeit von Fußgängerübergängen, insbesondere Zebrastreifen und Fußgängerampeln, mit einem Deep Learning Ansatz. Um zu analysieren, ob unsere erstellten Ansätze und Algorithmen einen tatsächlichen Mehrwert für Menschen mit Sehschädigung erzeugen, vollziehen wir ein kleines Wizard-of-Oz-Experiment zu unserem bedarfsgerechten Routing — mit einem sehr ermutigendem Ergebnis. Weiterhin führen wir eine umfangreichere Studie mit verschiedenen Komponenten und dem Fokus auf Fußgängerübergänge durch. Obwohl unsere statistischen Auswertungen nur eine geringfügige Verbesserung aufzeigen, beeinflußt durch technische Probleme mit dem ersten Prototypen und einer zu geringen Eingewöhnungszeit der Probanden an das System, bekommen wir viel versprechende Kommentare von fast allen Studienteilnehmern. Dies zeigt, daß wir bereits einen wichtigen ersten Schritt zum Schließen der identifizierten Lücke geleistet haben und Orientierung&Mobilität für Menschen mit Sehschädigung damit verbessern konnten
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