With the fast development of embedded deep-learning computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying NN onto the devices under complex environments, there are various types of possible faults: soft errors caused by cosmic radiation and radioactive impurities, voltage instability, aging, temperature variations, etc. Thus, more attention is drawn on the reliability of the NN embedded system. In this project, we build a virtual simulation system in Gazebo to simulate and test the working of an embedded NN system in the virtual environment in indoor navigation. The system can detect objects in the virtual environment with the help of the virtual camera(the image sensor) and the object detection module, which is based on YOLO v3, and make corresponding control decisions. We also designed and simulated the corresponding error injection module according to the working principle of the image sensor, and tested the functionality, and fault tolerance of the YOLO network. At the same time, network pruning algorithm is also introduced to study the relationship between different degrees of network pruning and network fault tolerance to sensor faults. / Med den snabba utvecklingen av inbyggda datorsystem för djupinlärning flyttas applikationer som drivs av djupinlärning från molnet till kanten. När man distribuerar NN på enheterna under komplexa miljöer finns det olika typer av möjliga fel: mjuka fel orsakade av kosmisk strålning och radioaktiva föroreningar, spänningsinstabilitet, åldrande, temperaturvariationer, illvilliga angripare, etc. Därför är mer uppmärksamhet ritade om tillförlitligheten hos det inbyggda NN-systemet. I det här projektet bygger vi ett virtuellt simuleringssystem för att simulera och testa hur ett inbäddat NN-system fungerar i den virtuella miljö vi ställer upp. Systemet kan upptäcka objekt i den virtuella miljön enligt den virtuella kameran och objektdetekteringsmodulen, som är baserad på YOLO v3, och göra motsvarande kontrollstrategier. Vi designade och simulerade också motsvarande felinsprutningsmodul enligt bildsensorns arbetsprincip och testade funktionalitet, tillförlitlighet och feltolerans hos YOLO-nätverket. Samtidigt nätverk beskärningsalgoritm introduceras också för att studera sambandet mellan olika grader av nätverksbeskärning och nätverksfeltolerans.

This thesis addresses the issues of data sparsity in the sonar domain. A data pipeline is set up to generate and enhance sonar data. The possibilities and limitations of using cycleGAN as a tool to enhance simulated sonar images for the purpose of training neural networks for detection and classification is studied. A neural network is trained on the enhanced simulated sonar images and tested on real sonar images to evaluate the quality of these images.The novelty of this work lies in extending previous methods to a more general framework and showing that GAN enhanced simulations work for complex tasks on field data.Using real sonar images to enhance the simulated images, resulted in improved classification compared to a classifier trained on solely simulated images. / Denna rapport ämnar undersöka problemet med gles data för djupinlärning i sonardomänen. Ett dataflöde för att generera och höja kvalitén hos simulerad sonardata sätts upp i syfte att skapa en stor uppsättning data för att träna ett neuralt nätverk. Möjligheterna och begränsningarna med att använda cycleGAN för att höja kvalitén hos simulerad sonardata studeras och diskuteras. Ett neuralt nätverk för att upptäcka och klassificera objekt i sonarbilder tränas i syfte att evaluera den förbättrade simulerade sonardatan.Denna rapport bygger vidare på tidigare metoder genom att generalisera dessa och visa att metoden har potential även för komplexa uppgifter baserad på icke trivial data.Genom att träna ett nätverk för klassificering och detektion på simulerade sonarbilder som använder cycleGAN för att höja kvalitén, ökade klassificeringsresultaten markant jämfört med att träna på enbart simulerade bilder.

Machine learning has been employed in the automotive industry together with cameras to detect objects in surround sensing technology. You Only Look Once is a state-of-the-art object detection algorithm especially suitable for real-time applications due to its speed and relatively high accuracy compared to competing methods. Recent studies have investigated whether radar data can be used as an alternative to camera data with You Only Look Once, seeing as radars are more robust to changing environments such as various weather and lighting conditions. These studies have used 3D data from radar consisting of range, angle, and velocity, transformed into a 2D image representation, either in the Range-Angle or Range-Doppler domain. Furthermore, the processed radar image can use either a Cartesian or a polar coordinate system for the rendering. This study will combine previous studies, using You Only Look Once with Range-Angle radar images and examine which coordinate system of Cartesian or polar is most optimal. Additionally, evaluating the localization and classification performance will be done using a combination of concepts and evaluation metrics. Ultimately, the conclusion is that the Cartesian coordinate system prevails with asignificant improvement compared to polar. / Maskininlärning har sedan länge använts inom fordinsindustrin tillsammans med kameror för att upptäcka föremål och få en ökad överblick över omgivningar. You Only Look Once är en toppmodern objektdetekteringsalgoritm särskilt lämplig för realtidsapplikationer tack vare dess hastighet och relativt höga noggrannhet jämfört med konkurrerande metoder. Nyligen genomförda studier har undersökt om radardata kan användas som ett alternativ till kameradata med You Only Look Once, eftersom radar är mer robusta för ändrade miljöer så som olika väder- och ljusförhållanden. Dessa studier har utnyttjat 3D data från radar bestående av avstånd, vinkel och hastighet, som transformerats till en 2D bildrepresentation, antingen i domänen Range-Angle eller Range-Doppler. Vidare kan den bearbetade radarbilden använda antingen ett kartesiskt eller ett polärt koordinatsystem för framställningen. Denna studie kommer att kombinera tidigare studier om You Only Look Once med Range-Angle radarbilder och undersöka vilket koordinatsystem, kartesiskt eller polärt, som är mest optimalt att använda för människodetektering med radar. Dessutom kommer en utvärdering av lokaliserings- och klassificeringsförmåga att göras med hjälp av en blandning av koncept och olika mått på prestanda. Slutsatsen gjordes att det kartesiska koordinatsystemet är det bättre alternativet med en betydligt högre prestanda jämfört med det polära koordinatsystemet.

yes / Coinciding with advances in whole-slide imaging scanners, it is become essential to automate the conventional image-processing techniques to assist pathologists with some tasks such as mitotic-cells detection. In histopathological images analysing, the mitotic-cells counting is a significant biomarker in the prognosis of the breast cancer grade and its aggressiveness. However, counting task of mitotic-cells is tiresome, tedious and time-consuming due to difficulty distinguishing between mitotic cells and normal cells. To tackle this challenge, several deep learning-based approaches of Computer-Aided Diagnosis (CAD) have been lately advanced to perform counting task of mitotic-cells in the histopathological images. Such CAD systems achieve outstanding performance, hence histopathologists can utilise them as a second-opinion system. However, improvement of CAD systems is an important with the progress of deep learning networks architectures. In this work, we investigate deep YOLO (You Only Look Once) v2 network for mitotic-cells detection on ICPR (International Conference on Pattern Recognition) 2012 dataset of breast cancer histopathology. The obtained results showed that proposed architecture achieves good result of 0.839 F1-measure.

Syftet med denna studie är att förbättra effektiviteten och noggrannheten hos YOLO-modeller genom att optimera dem, särskilt när de står inför begränsade datorresurser. Det akuta behovet av objektigenkänning i nära realtid i tillämpningar som övervakningssystem och autonom körning understryker betydelsen av bearbetningshastighet och exceptionell noggrannhet. Avhandlingen fokuserar på svårigheterna med att implementera komplexa modeller för objektidentifiering på enheter med låg kapacitet, nämligen Jetson Orin Nano. Den föreslår många optimeringsmetoder för att övervinna dessa hinder. Vi utförde flera försök och gjorde metodologiska förbättringar för att minska bearbetningskraven och samtidigt bibehålla en stark prestanda för objektdetektering. Viktiga komponenter i forskningen inkluderar noggrann modellträning, användning av bedömningskriterier och undersökning av optimeringseffekter på modellprestanda i verkliga miljöer. Studien visar att det är möjligt att uppnå optimal prestanda i YOLO-modeller trots begränsade resurser, vilket ger betydande framsteg inom datorseende och maskininlärning. / The objective of this study is to improve the efficiency and accuracy of YOLO models by optimizing them, particularly when faced with limited computing resources. The urgent need for near realtime object recognition in applications such as surveillance systems and autonomous driving underscores the significance of processing speed and exceptional accuracy. The thesis focuses on the difficulties of implementing complex object identification models on low-capacity devices, namely the Jetson Orin Nano. It suggests many optimization methods to overcome these obstacles. We performed several trials and made methodological improvements to decrease processing requirements while maintaining strong object detecting performance. Key components of the research include meticulous model training, the use of assessment criteria, and the investigation of optimization effects on model performance in reallife settings. The study showcases the feasibility of achieving optimal performance in YOLO models despite limited resources, bringing substantial advancements in computer vision and machine learning.

The thesis deals with the detection and classification of flying objects. The work can be divided into three parts. The first part describes the creation of dataset of flying objects. The reverse image search is used to create the dataset. The next part is a research of algorithms for detection, tracking and classification. Subsequently, the individual algorithms are applied and evaluated. In the last part, the design of hardware components is performed.

The main objective of this thesis is to propose a localization method for a UAV using various computer vision and machine learning techniques. It plays a major role in planning the strategy for the flight, and acts as a navigational contingency method, in event of a GPS failure. The implementation of the algorithms employs high processing capabilities of the graphics processing unit, making it more efficient. The method involves the working of various neural networks, working in synergy to perform the localization. This thesis is a part of a collaborative project between The University of North Texas, Denton, USA, and the University of Windsor, Ontario, Canada. The localization has been divided into three phases namely object detection, recognition, and location estimation. Object detection and position estimation were discussed in this thesis while giving a brief understanding of the recognition. Further, future strategies to aid the UAV to complete the mission, in case of an eventuality, like the introduction of an EDGE server and wireless charging methods, was also given a brief introduction.

Additive manufacturing (AM) allows building complex shapes with high accuracy. The X-ray Computed Tomography (XCT) is one of the promising non-destructive evaluation techniques for the evaluation of subsurface defects in an additively manufactured component. Automatic defect detection and segmentation methods can assist part inspection for quality control. However, automatic detection and segmentation of defects in XCT data of AM possess challenges due to contrast, size, and appearance of defects. In this research different deep learning techniques have been applied on publicly available XCT image datasets of additively manufactured cobalt chrome samples produced by the National Institute of Standards and Technology (NIST). To assist the data labeling image processing techniques were applied which are median filtering, auto local thresholding using Bernsen’s algorithm, and contour detection. A convolutional neural network (CNN) based state-of-art object algorithm YOLOv5 was applied for defect detection. Defect segmentation in XCT slices was successfully achieved applying U-Net, a CNN-based network originally developed for biomedical image segmentation. Three different variants of YOLOv5 which are YOLOv5s, YOLOv5m, and YOLOV5l were implemented in this study. YOLOv5s achieved defect detection mean average precision (mAP) of 88.45 % at an intersection over union (IoU) threshold of 0.5. And mAP of 57.78% at IoU threshold 0.5 to 0.95 using YOLOv5M was achieved. Additionally, defect detection recall of 87.65% was achieved using YOLOv5s, whereas a precision of 71.61 % was found using YOLOv5l. YOLOv5 and U-Net show promising results for defect detection and segmentation respectively. Thus, it is found that deep learning techniques can improve the automatic defect detection and segmentation in XCT data of AM.

The need for products has increased during the last few years, this high demand needs to bemet with higher means of production. The use of neural networks can be the key to increasedproduction without having to compromise product quality or human workers well being. This thesislooks into the concept of reliable architectures in convolutional neural networks and how they canbe implemented. The neural networks are trained to recognize the features in images to identifycertain objects, these recognition is then compared to other models to see which of them had the bestprediction. Using multiple models creates a reliable architecture from which results can be produced,these results can then be used in combinations with algorithms to improve prediction certainty. Theaim of implementing the networks with these algorithms are to improve the results without havingto change the networks configurations.

The objective of the thesis is to explore an approach of classifying and localizing different objects from driving-scene images using YOLOv4 algorithm trained on custom dataset.  YOLOv4, a one-stage object detection algorithm, aims to have better accuracy and speed. The deep learning (convolutional) network based classification model was trained and validated on a subject of SODA10M dataset annotated with six different classes of objects (Car, Cyclist, Truck, Bus, Pedestrian, and Tricycle), which are the most seen objects on the road. Another model based on YOLOv3 (the previous version of YOLOv4) will be trained on the same dataset and the performance will be compared with the YOLOv4 model. Both algorithms are fast but have difficulty detecting some objects, especially the small objects. Larger quantities of properly annotated training data can improve the algorithm's performance accuracy.