This master's thesis deals with a vehicle detector based on the convolutional neural network and scene captured by drone. Dataset is described at the beginning, because the main aim of this thesis is to create practicly usable detector. Architectures of the forward neural networks which detector was created from are described in the next chapter. Techniques for building a detector based on the naive methods and current the most successful meta architectures follow the neural network architectures. An implementation of the detector is described in the second part of this thesis. The final detector was built on meta architecture Faster R-CNN and PVA neural network on which the detector achieved score over 90 % and 45 full HD frames per seconds.

Tato práce se zabývá detekcí chodců pomocí konvolučních neuronových sítí z pohledu autonomního vozidla. A to zejména jejich otestováním ve smyslu nalezení vhodné praxe tvorby datasetu pro machine learning modely. V práci bylo natrénováno celkem deset machine learning modelů meta architektur Faster R-CNN s ResNet 101 jako feature extraktorem a SSDLite s feature extraktorem MobileNet_v2. Tyto modely byly natrénovány na datasetech o různých velikostech. Nejlépší výsledky byly dosaženy na datasetu o velikosti 5000 snímků. Kromě těchto modelů byl vytvořen nový dataset zaměřující se na chodce v noci. Dále byla vytvořena knihovna Python funkcí pro práci s datasety a automatickou tvorbu datasetu.

The goal of this thesis is to design and implement algorithm for text layout analysis in historical documents. Neural network was used to solve this problem, specifically architecture Faster-RCNN. Dataset of 6 135 images with historical newspaper was used for training and testing. For purpose of the thesis four models of neural networks were trained: model for detection of words, headings, text regions and model for words detection based on position in line. Outputs from these models were processed in order to determine text layout in input image. A modified F-score metric was used for the evaluation. Based on this metric, the algorithm reached an accuracy almost 80 %.

I projektet detekteras och följs granplantor i spatiospektrala bilder för att därefter skapa en hyperspektral datakub för av varje gran. För att detektera granarna prövas fyra metoder: manuell detektion, detektion med segmentering, detektion med SVM och detektion med neuralt nätverk. Minnesanvändning och körningstid jämförs mellan två implementationer, där hyperspektral rekonstruktion görs med olika metoder.

I projektet detekteras och följs granplantor i spatiospektrala bilder för att därefter skapa en hyperspektral datakub för av varje gran. För att detektera granarna prövas fyra metoder: manuell detektion, detektion med segmentering, detektion med SVM och detektion med neuralt nätverk. Minnesanvändning och körningstid jämförs mellan två implementationer, där hyperspektral rekonstruktion görs med olika metoder.

This paper concerns itself with object detection as a possible solution to Valmet’s quest for a visual-feedback system that can help operators and other personnel to more easily interact with their machines and equipment. New advancements in deep learning, specifically CNN models, have been exploring neural networks with detection-capabilities. Object detection has historically been mostly inaccessible to the industry due the complex solutions involving various tricky image processing algorithms. In that regard, deep learning offers a more easily accessible way to create scalable object detection solutions. This study has therefore chosen to review recent literature detailing detection models with a selective focus on factors making them realizable on ARM hardware and in turn mobile devices like phones. An attempt was made to single out the most lightweight and hardware efficient model and implement it as a prototype in order to help Valmet in their decision process around future object detection products. The survey led to the choice of a SSD-MobileNetsV2 detection architecture due to promising characteristics making it suitable for performance-constrained smartphones. This CNN model was implemented on Valmet’s phone of choice, Samsung Galaxy S8, and it successfully achieved object detection functionality. Evaluation shows a mean average precision of 60 % in detecting objects and a 4.7 FPS performance on the chosen phone model. TensorFlow was used for developing, training and evaluating the model. The report concludes with recommending Valmet to pursue solutions built on-top of these kinds of models and further wishes to express an optimistic outlook on this type of technology for the future. Realizing performance of this magnitude on a mid-tier phone using deep learning (which historically is very computationally intensive) sets us up for great strides with this type of technology in the future; and along with better smartphones, great benefits are expected to both industry and consumers. / Den här rapporten behandlar objekt detektering som en möjlig lösning på Valmets efterfrågan av ett visuellt återkopplingssystem som kan hjälpa operatörer och annan personal att lättare interagera med maskiner och utrustning. Nya framsteg inom djupinlärning har dem senaste åren möjliggjort framtagande av neurala nätverksarkitekturer med detekteringsförmågor. Då industrisektorn svårare tar till sig högst specialiserade algoritmer och komplexa bildbehandlingsmetoder (som tidigare varit fallet med objekt detektering) så ger djupinlärningsmetoder istället upphov till att skapa självlärande system som är återanpassningsbara och närmast intuitiva i dem fall där sådan teknologi åberopas. Den här studien har därför valt att studera ett par sådana teknologier för att hitta möjliga implementeringar som kan realiseras på något så enkelt som en mobiltelefon. Urvalet har därför bestått i att hitta detekteringsmodeller som är hårdvarumässigt resurssnåla och implementera ett sådant system för att agera prototyp och underlag till Valmets vidare diskussioner kring objekt-detekteringsslösningar. Studien valde att implementera en SSD-MobileNetsV2 modellarkitektur då den uppvisade lovande egenskaper kring hårdvarukraven. Modellen implementerades och utvärderades på Valmets mest förekommande telefon Samsung Galaxy S8 och resultatet visade på en god förmåga för modellen att detektera objekt. Den valda modellen gav 60 % precision på utvärderingsbilderna och lyckades nå 4.7 FPS på den implementerade telefonen. TensorFlow användes för programmering och som stödjande mjukvaruverktyg för träning, utvärdering samt vidare implementering. Studien påpekar optimistiska förväntningar av denna typ av teknologi; kombinerat med bättre smarttelefoner i framtiden kan det leda till revolutionerande lösningar för både industri och konsumenter.

Aufgrund des Mißerfolges der Mars Observer Mission 1992 und allgemeiner sinkender Raumfahrtetats, entwickelte NASA 1995 die „Faster Better Cheaper“ (FBC) Philosophie. Diese sah vor, daß planetare Missionen innerhalb eines kurzen Zeitraumes und mit begrenzten Budgets geplant, gebaut, getestet und gestartet werden sollten. Dabei sollten neue Technologien und neue Betriebsmethoden zum Einsatz kommen. Mögliche Fehlschläge durch unerprobte Instrumente oder Prozesse wurden dabei nicht ausgeschlossen. Der Mißerfolg der Mars-Missionen im Jahr 1999 und weiterer Projekte zwangen jedoch zu einem Umdenken der „Faster Better Cheaper“ Philosophie. Eine Vielzahl von Abhandlungen und Untersuchungen wurden daraufhin veröffentlicht, die Fehler der FBC Philosophie aufzeigten, ohne dabei jedoch auf mögliche Verbesserungen einzugehen. Das Ziel dieser Arbeit besteht in der Ermittlung effektiver Maßnahmen, so daß Ressourcen während des Lebenszyklus eines Projektes optimal eingesetzt werden können. Aus der Analyse der fehlgeschlagenen Missionen und einer Erläuterung der Funktionen verschiedener planetarer Missionskonzepte, werden mögliche Maßnahmen zur Verringerung der Kosten ermittelt. Die Effektivität dieser Maßnahmen wird anhand eines Bewertungskataloges im Rahmen einer Simulation zu verschiedenen Zeitpunkten einer Mission bestimmt. Es wird dabei eine Handlungshilfe erstellt, mit der ein Projektmanager die Verteilung von Ressourcen optimieren kann. Die Systemtechnik bietet hierzu eine Vielzahl von Analyse- und Simulationsmethoden, mit der die hier gemachten Angaben bewertet und überprüft werden können. / Due to the failure of the Mars Observer Mission in 1992 and decreasing budgets, NASA developed a new philosophy for the development, design and operations called „Faster Better Cheaper“ (FBC). New technologies and new management methods were deployed to reduce lift cycle costs. Possible mission failures were expected. After the losses of the Mars Missions in 1999 and other missions, NASA was forced to rethink its FBC approach. Numerous papers have been published in the meantime which identified the mistakes of the missions and of FBC, but none have identified potential improvements. The objective of this paper is the development of potential measurements for the design of the operations of unmanned space missions that should be applied during its life cycles. A new tool in form of an EXCEL spreadsheet will be developed based on historical missions, which can be used a program manager who can allocate resources in optimal way. Systems Engineering Techniques will be used in various ways to identify problems and to measure potential improvements.

Foreign objects in Chest X-ray (CXR) cause complications during automatic image processing. To prevent errors caused by these foreign objects, it is necessary to automatically find them and ommit them in the analysis. These are mainly buttons, jewellery, implants, wires and tubes. At the same time, finding pacemakers and other placed devices can help with automatic processing. The aim of this work was to design a method for the detection of foreign objects in CXR. For this task, Faster R-CNN method with a pre-trained ResNet50 network for feature extraction was chosen which was trained on 4 000 images and lately tested on 1 000 images from a publicly available database. After finding the optimal learning parameters, it was managed to train the network, which achieves 75% precision, 77% recall and 76% F1 score. However, a certain part of the error is formed by non-uniform annotations of objects in the data because not all annotated foreign objects are located in the lung area, as stated in the description.

Enhanced vision and object detection could be useful in the aviation domain in situations of bad weather or cluttered environments. In particular, enhanced vision and object detection could improve situational awareness and aid the pilot in environment interpretation and detection of hazardous objects. The fundamental concept of object detection is to interpret what objects are present in an image with the aid of a prediction model or other feature extraction techniques. Constructing a comprehensive data set that can describe the operational environment and be robust for weather and lighting conditions is vital if the object detector is to be utilised in the avionics domain. Evaluating the accuracy and robustness of the constructed data set is crucial. Since erroneous detection, referring to the object detection algorithm failing to detect a potentially hazardous object or falsely detecting an object, is a major safety issue. Bayesian uncertainty estimations are evaluated to examine if they can be utilised to detect miss-classifications, enabling the use of a Bayesian Neural Network with the object detector to identify an erroneous detection. The object detector Faster RCNN with ResNet-50-FPN was utilised using the development framework Detectron2; the accuracy of the object detection algorithm was evaluated based on obtained MS-COCO metrics. The setup achieved a 50.327 % AP@[IoU=.5:.95] score. With an 18.1 % decrease when exposed to weather and lighting conditions. By inducing artificial artefacts and augmentations of luminance, motion, and weather to the images of the training set, the AP@[IoU=.5:.95] score increased by 15.6 %. The inducement improved the robustness necessary to maintain the accuracy when exposed to variations of environmental conditions, which resulted in just a 2.6 % decrease from the initial accuracy. To fully conclude that the augmentations provide the necessary robustness for variations in environmental conditions, the model needs to be subjected to actual image representations of the operational environment with different weather and lighting phenomena. Bayesian uncertainty estimations show great promise in providing additional information to interpret objects in the operational environment correctly. Further research is needed to conclude if uncertainty estimations can provide necessary information to detect erroneous predictions.

The Swedish armed forces use the Single Source Intelligent Cell (SSIC), developed by Saab, for analysis of aerial reconnaissance video and report generation. The analysis can be time-consuming and demanding for a human operator. In the analysis workflow, identifying vehicles is an important part of the work. Artificial Intelligence is widely used for analysis in many industries to aid or replace a human worker. In this paper, the possibility to aid the human operator with air reconnaissance data analysis is investigated, specifically, object detection for finding cars in aerial images. Many state-of-the-art object detection models for vehicle detection in aerial images are based on a Convolutional Neural Network (CNN) architecture. The Faster R-CNN- and SSD-based models are both based on this architecture and are implemented. Comprehensive experiments are conducted using the models on two different datasets, the open Video Verification of Identity (VIVID) dataset and a confidential dataset provided by Saab. The datasets are similar, both consisting of aerial images with vehicles. The initial experiments are conducted to find suitable configurations for the proposed models. Finally, an experiment is conducted to compare the performance of a human operator and a machine. The results from this work prove that object detection can be used to supporting the work of air reconnaissance image analysis regarding inference time. The current performance of the object detectors makes applications, where speed is more important than accuracy, most suitable.