Ultrasonic sensors are commonly used in automobiles to assist driving maneuvers, e.g., parking, because of their cost-effectiveness and robustness. This thesis investigated the feasibility of using an Ultrasonic Sensor Array to locate the 3D position of an object and also using the measurements from the sensor array to train a Convolutional Neural Network (CNN) to classify the objects. A simulated Ultrasonic Sensor array was built in COMSOL Multiphysics. The simulation of ultrasound used Ray Tracing technology to track the path of ultrasound rays. The readouts from the sensor array are used to formulate an optimization problem to address the 3D positioning of the object. We investigated the performance of two optimization methods in terms of the accuracy of the prediction and the efficiency of solving the problem. The average mean absolute error (MAE) and average mean squared error (MSE) of the Nelder-Mead method (without constraints) are 2.66 mm and 12.79 mm2 respectively, the average running time to predict one 3D position is 97.62 ms. The average MAE and average MSE of Powell’s method (with constraints) are 2.84 mm and 23.66 mm2 respectively, average running time to predict one 3D position is 84.68 ms. The result of Powell’s method (without constraints) is much worse than the above two, its average MAE and MSE are 24.93 mm and 7559.46 mm2, average running time is 238.30 ms. The readouts from the sensor array are also used to build eight different datasets of which the data structures are different combinations of the information from the readouts. Each of these eight data sets is used to train a CNN, and the classification accuracy of each CNN indicates that how well the data structure represents the objects. The results showed that the CNN trained by stacked time array 5×5×3 had the best classification accuracy among eight datasets, the classification accuracy on the test set is 85.05%.

The aim of this master's thesis thesis is to enable evaluation of quality, or the type of product in industrial applications using artificial neural networks, especially in applications where the classical approach of machine vision is too complicated. The system thus designed is implemented onto a specific hardware platform and becomes a subject to the final optimalisation for the hardware platform for the best performance of the system.

The ROC (Receiver Operating Characteristic) curve is a projection of two different cumulative distribution functions F0 and F1. On axis are values 1-F0(c) and 1-F1(c). The c-parameter is a real number. This curve is useful to check quality of discriminant rule which classify an object to one of two classes. The criterion is a size of an area under the curve. To solve real problems we use point and interval estimation of ROC curves and statistical hypothesis tests about ROC curves.

With the wide application of machine learning technologies in advanced driver assistance systems of vehicles, object classification on obstacles has attracted much attention. Ultrasonic sensors are mainly used to measure the distance to obstacles under the condition of low-speed movement. The existing ultrasonic sensor is low-cost, and its good performance and robustness are sufficient for obstacle avoidance. Recent progress on ultrasonic has attempted to classify obstacles with the combination of ultrasonic sensors and machine learning. It shows that deep neural networks are able to classify objects using only ultrasonic sensors. In the thesis, we focus on the object classification on sizes of obstacles and expect our proposed neural networks model can solve the classification task under the simulation environment, thus contributing to the application of ultrasonic sensors in vehicles. The ultrasonic sensor arrays are built in COMSOL Multiphysics and can provide ultrasonic data with different objects. After many simulation experiments, the ultra-sonic data from objects are labeled and stored in datasets. Then we process the ultrasonic data from datasets and feed them to the proposed neural networks. The ultrasonic data obtained by experiments are examined by the distribution of reflected ultrasonic rays in simulation. The analysis results are in line with our expectations. The trained neural networks are divided into two groups. The first networks group is trained with data from cubes and shows over 80% accuracy on five object categories. The second group of networks is trained with data from cubes and S1 objects. There is an approximate 5% drop in classification performance as the difficulty of the classification task increases.

The aim of the present work is to select a suitable object classification method for the recognition of one-handed finger alphabet characters. For this purpose, a sufficiently robust dataset has been created and is included in this work. The creation of the dataset is necessary for training the convolutional neural network. Further more, a suitable topology for data classification was found. The whole work is implemented using Python and the open-source library Keras was used.

<div> <div> <p>Building Information Modeling (BIM) serves as an important media in supporting automation in the architecture, engineering, and construction (AEC) domain. However, with its fast development by different software companies in different applications, data exchange became labor-intensive, costly, and error-prone, which is known as the problem of interoperability. Industry foundation classes (IFC) are widely accepted to be the future of BIM in solving the challenge of BIM interoperability. However, there are practical limitations of the IFC standards, e.g., IFC’s flexibility creates space for misuses of IFC entities. This incorrect semantic information of an object can cause severe problems to downstream uses. To address this problem, the author proposed to use the concept of invariant signatures, which are a new set of features that capture the essence of an AEC object. Based on invariant signatures, the author proposed a rule-based method and a machine learning method for BIM-based AEC object classification, which can be used to detect potential misuses automatically. Detailed categories for beams were tested to have error-free performance. The best performing algorithm developed by the methods achieved 99.6% precision and 99.6% recall in the general building object classification. To promote automation and further improve the interoperability of BIM tasks, the author adopted invariant signature-based object classification in quantity takeoff (QTO), structural analysis, and model validation for automated building code compliance checking (ACC). Automation in such BIM tasks was enabled with high accuracy.</p><p><br></p><p><br></p> </div> </div>

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Poängräkning och resultatanalys vid skytteträning är en viktig aspekt i utvecklingen av skyttens skjutförmåga. En bildbaserad träffpunktdetektionsalgoritm skulle automatisera denna process och bidra med digital presentation av resultatet. Befintliga lösningar är högkvalitativa metoder som detekterar träffpunkter med hög precision. Dessa lösningar är dock anpassade efter orealistiska användningsfall där måltavlor i gott skick som beskjutits vid ett enda tillfälle fotograferas i gynnsamma miljöer. Realistiska skytteförhållanden förekommer utomhus där träffpunkterna täcks med klisterlappar mellan skottrundorna och måltavlorna återanvänds tills dem faller sönder. Detta kandidatarbete introducerar en metod för automatisk detektion av träffar anpassad efter realistiska användningssituationer och bygger på tillgängliga bildanalystekniker. Den föreslagna metoden detekterar punkter med 40 procent noggrannhet i lågkvalitativa måltavlor och uppnår 88 procents noggrannhet i måltavlor av hög kvalitet. Dock produceras ett betydande antal falska positiva utfall. Resultatet påvisar möjligheten att utveckla ett sådant system och belyser de problem som tillkommer en sådan implementation. / Score calculation and performance analysis on shooting targets is an important aspect in development of shooting ability. An image based automatic scoring algorithm would provide automation of this procedure and digital visualization of the result. Prevailing solutions are high quality algorithms detecting hit points with high precision. However, these methods are adapted to unrealistic use cases where single-used, high-quality target boards are photographed in favourable environments. Usually gun shooting is performed outdoors where bullet holes are covered with stickers between shooting rounds, and targets are reused until they fall apart. This bachelor thesis introduces a method of automatic hit point detection adapted to realistic shooting conditions and relies solely on available image processing techniques. The proposed algorithm detects holes with 40 percent detection rate in low-quality target boards, reaching 88 percent detection rate in targets of higher quality. However, producing a significant number of false positives. The result demonstrates the possibilities of developing such a system and highlights the difficulties associated with such an implementation.

This Ph.D. thesis tackles the issue of sill and video object categorization. The objective is to associate semantic labels to 2D objects present in natural images/videos. The principle of the proposed approach consists of exploiting categorized 3D model repositories in order to identify unknown 2D objects based on 2D/3D matching techniques. We propose here an object recognition framework, designed to work for real time applications. The similarity between classified 3D models and unknown 2D content is evaluated with the help of the 2D/3D description. A voting procedure is further employed in order to determine the most probable categories of the 2D object. A representative viewing angle selection strategy and a new contour based descriptor (so-called AH), are proposed. The experimental evaluation proved that, by employing the intelligent selection of views, the number of projections can be decreased significantly (up to 5 times) while obtaining similar performance. The results have also shown the superiority of AH with respect to other state of the art descriptors. An objective evaluation of the intra and inter class variability of the 3D model repositories involved in this work is also proposed, together with a comparative study of the retained indexing approaches . An interactive, scribble-based segmentation approach is also introduced. The proposed method is specifically designed to overcome compression artefacts such as those introduced by JPEG compression. We finally present an indexing/retrieval/classification Web platform, so-called Diana, which integrates the various methodologies employed in this thesis

Pour agir de manière autonome et intelligente dans un environnement, un robot mobile doit disposer de cartes. Une carte contient les informations spatiales sur l’environnement. La géométrie 3D ainsi connue par le robot est utilisée non seulement pour éviter la collision avec des obstacles, mais aussi pour se localiser et pour planifier des déplacements. Les robots de prochaine génération ont besoin de davantage de capacités que de simples cartographies et d’une localisation pour coexister avec nous. La quintessence du robot humanoïde de service devra disposer de la capacité de voir comme les humains, de reconnaître, classer, interpréter la scène et exécuter les tâches de manière quasi-anthropomorphique. Par conséquent, augmenter les caractéristiques des cartes du robot à l’aide d’attributs sémiologiques à la façon des humains, afin de préciser les types de pièces, d’objets et leur aménagement spatial, est considéré comme un plus pour la robotique d’industrie et de services à venir. Une carte sémantique enrichit une carte générale avec les informations sur les entités, les fonctionnalités ou les événements qui sont situés dans l’espace. Quelques approches ont été proposées pour résoudre le problème de la cartographie sémantique en exploitant des scanners lasers ou des capteurs de temps de vol RGB-D, mais ce sujet est encore dans sa phase naissante. Dans cette thèse, une tentative de reconstruction sémantisée d’environnement d’intérieur en utilisant une caméra temps de vol qui ne délivre que des informations de profondeur est proposée. Les caméras temps de vol ont modifié le domaine de l’imagerie tridimensionnelle discrète. Elles ont dépassé les scanners traditionnels en termes de rapidité d’acquisition des données, de simplicité fonctionnement et de prix. Ces capteurs de profondeur sont destinés à occuper plus d’importance dans les futures applications robotiques. Après un bref aperçu des approches les plus récentes pour résoudre le sujet de la cartographie sémantique, en particulier en environnement intérieur. Ensuite, la calibration de la caméra a été étudiée ainsi que la nature de ses bruits. La suppression du bruit dans les données issues du capteur est menée. L’acquisition d’une collection d’images de points 3D en environnement intérieur a été réalisée. La séquence d’images ainsi acquise a alimenté un algorithme de SLAM pour reconstruire l’environnement visité. La performance du système SLAM est évaluée à partir des poses estimées en utilisant une nouvelle métrique qui est basée sur la prise en compte du contexte. L’extraction des surfaces planes est réalisée sur la carte reconstruite à partir des nuages de points en utilisant la transformation de Hough. Une interprétation sémantique de l’environnement reconstruit est réalisée. L’annotation de la scène avec informations sémantiques se déroule sur deux niveaux : l’un effectue la détection de grandes surfaces planes et procède ensuite en les classant en tant que porte, mur ou plafond; l’autre niveau de sémantisation opère au niveau des objets et traite de la reconnaissance des objets dans une scène donnée. A partir de l’élaboration d’une signature de forme invariante à la pose et en passant par une phase d’apprentissage exploitant cette signature, une interprétation de la scène contenant des objets connus et inconnus, en présence ou non d’occultations, est obtenue. Les jeux de données ont été mis à la disposition du public de la recherche universitaire. / Intelligent autonomous actions in an ordinary environment by a mobile robot require maps. A map holds the spatial information about the environment and gives the 3D geometry of the surrounding of the robot to not only avoid collision with complex obstacles, but also selflocalization and for task planning. However, in the future, service and personal robots will prevail and need arises for the robot to interact with the environment in addition to localize and navigate. This interaction demands the next generation robots to understand, interpret its environment and perform tasks in human-centric form. A simple map of the environment is far from being sufficient for the robots to co-exist and assist humans in the future. Human beings effortlessly make map and interact with environment, and it is trivial task for them. However, for robots these frivolous tasks are complex conundrums. Layering the semantic information on regular geometric maps is the leap that helps an ordinary mobile robot to be a more intelligent autonomous system. A semantic map augments a general map with the information about entities, i.e., objects, functionalities, or events, that are located in the space. The inclusion of semantics in the map enhances the robot’s spatial knowledge representation and improves its performance in managing complex tasks and human interaction. Many approaches have been proposed to address the semantic SLAM problem with laser scanners and RGB-D time-of-flight sensors, but it is still in its nascent phase. In this thesis, an endeavour to solve semantic SLAM using one of the time-of-flight sensors which gives only depth information is proposed. Time-of-flight cameras have dramatically changed the field of range imaging, and surpassed the traditional scanners in terms of rapid acquisition of data, simplicity and price. And it is believed that these depth sensors will be ubiquitous in future robotic applications. In this thesis, an endeavour to solve semantic SLAM using one of the time-of-flight sensors which gives only depth information is proposed. Starting with a brief motivation in the first chapter for semantic stance in normal maps, the state-of-the-art methods are discussed in the second chapter. Before using the camera for data acquisition, the noise characteristics of it has been studied meticulously, and properly calibrated. The novel noise filtering algorithm developed in the process, helps to get clean data for better scan matching and SLAM. The quality of the SLAM process is evaluated using a context-based similarity score metric, which has been specifically designed for the type of acquisition parameters and the data which have been used. Abstracting semantic layer on the reconstructed point cloud from SLAM has been done in two stages. In large-scale higher-level semantic interpretation, the prominent surfaces in the indoor environment are extracted and recognized, they include surfaces like walls, door, ceiling, clutter. However, in indoor single scene object-level semantic interpretation, a single 2.5D scene from the camera is parsed and the objects, surfaces are recognized. The object recognition is achieved using a novel shape signature based on probability distribution of 3D keypoints that are most stable and repeatable. The classification of prominent surfaces and single scene semantic interpretation is done using supervised machine learning and deep learning systems. To this end, the object dataset and SLAM data are also made publicly available for academic research.