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In recent years, AI has developed significantly and become more popular than ever before. The applications of AI are expanding, making knowledge about its application and the systems it can be applied to more important. This project compares and evaluates deep learning models for object detection of traffic signs on the embedded systems Nvidia Jetson Nano and Raspberry Pi 3 Model B. The project compares and evaluates the models YOLOv5, SSD Mobilenet V1, FOMO, and Efficientdet-lite0. The project evaluates the performance of these models on the aforementioned embedded systems, measuring metrics such as CPU usage, FPS and RAM. Deep learning models are resource-intensive, and embedded systems have limited resources. Embedded systems often have different types of processor architectures than regular computers, which means that some frameworks and libraries may not be compatible. The results show that the tested systems are capable of object detection but with varying performance. Jetson Nano performs at a level we consider sufficiently high for use in production depending on the specific requirements. Raspberry Pi 3 performs at a level that may not be acceptable for real-time recognition of traffic signs. We see the greatest potential for Efficientdet-lite0 and YOLOv5 in recognizing traffic signs. The distance at which the models detect signs seems to be important for how many signs they find. For this reason, SSD MobileNet V1 is not recommended without further trai-ning despite its superior speed. YOLOv5 stood out as the model that detected signs at the longest distance and made the most detections overall. When considering all the results, we believe that Efficientdet-lite0 is the model that performs the best. / Under de senaste åren har AI utvecklats mycket och blivit mer populärt än någonsin. Tillämpningsområdena för AI ökar och därmed blir kunskap om hur det kan tillämpas och på vilka system viktigare. I det här projektet jämförs och utvärderas djupinlärningsmodeller för objektdetektering av trafikskyltar på de inbyggda systemen Nvidia Jetson Nano och Raspberry Pi 3 Model B. Modellerna som jämförs och utvärderas är YOLOv5, SSD Mobilenet V1, FOMO och Efficientdet-lite0. För varje modell mäts blandannat CPU-användning, FPS och RAM. Modeller för djupinlärning är resurskrävande och inbyggda system har begränsat med resurser. Inbyggda system har ofta andra typer av processorarkitekturer än en vanlig dator vilket gör att olika ramverk och andra bibliotek inte är kompatibla. Resultaten visar att de testade systemen klarar av objektdetektering med varierande prestation. Jetson Nano presterar på en nivå vi anser vara tillräckligt hög för användning i produktion beroende på hur hårda krav som ställs. Raspberry Pi 3 presterar på en nivå som möjligtvis inte är acceptabel för igenkänning av trafikskyltar i realtid. Vi ser störst potential för Efficientdet-lite0 och YOLOv5 för igenkänning av trafikskyltar. Hur långt avstånd modellerna upptäcker skyltar på verkar vara viktigt för hur många skyltar de hittar. Av den anledningen är SSD MobileNet V1 inte att rekommendera utan vidare träning trots sin överlägsna hastighet. YOLOv5 utmärkte sig som den som upptäckte skyltar på längst avstånd och som gjorde flest upptäckter totalt. När alla resultat vägs in anser vi dock att Efficientdet-lite0 är den modell som presterar bäst.

<p dir="ltr">A camera's image signal processor (ISP) is responsible for taking the mosaiced and noisy image signal from the image sensor and processing it such a way that an end-result image is produced that is informative and accurately captures the scene. Real-time video capture in photon-limited environments remains a challenge for many ISP's today. In these conditions, the image signal is dominated by the photon shot noise. Deep learning methods show promise in extracting the underlying image signal from the noise, but modern AI-based ISPs are too computationally complex to be realized as a fast and efficient hardware ISP. An ISP algorithm, BLADE2 has been designed, which leverages AI in a computationally conservative manner to demosaic and denoise low-light images. The original implementation of this algorihtm is in Python/PyTorch. This Thesis explores taking BLADE2 and implementing it on a general purpose GPU via a suite of Nvidia optimization toolkits, as well as a low-level implementation in C/C++, bringing the algorithm closer to FPGA realization. The GPU implementation demonstrated significant throughput gains and the C/C++ implementation demonstrated the feasibility of further hardware development.</p>

For 500 years transient light phenomena (TLP) have been observed on the lunar surface by ground-based observers. The actual physical reason for most of these events is today still unknown. Current plans of NASA and SpaceX to send astronauts back to the Moon and already successful deep-space CubeSat mission will allow in the future research nanosatellite missions to the cislunar space. This thesis presents a new hardware and software concept for a future payload on such a nanosatellite. The main task was to develop and implement a high-performance image processing algorithm which task is to detect short brightening flashes on the lunar surface. Based on a review of historic reported phenomena, possible explanation theories for these phenomena and currently active and planed ground- or space-based observatories possible reference scenarios were analyzed. From the presented scenarios one, the detection of brightening events was chosen and requirements for this scenario stated. Afterwards, possible detectors, processing computers and image processing algorithms were researched and compared regarding the specified requirements. This analysis of available algorithm was used to develop a new high-performance detection algorithm to detect transient brightening events on the Moon. The implementation of this algorithm running on the processor and the internal GPU of a MacMini achieved a framerate of 55 FPS by processing images with a resolution of 4.2 megapixel. Its functionality and performance was verified on the remote telescope operated by the Chair of Space Technology of the University of Würzburg. Furthermore, the developed algorithm was also successfully ported on the Nvidia Jetson Nano and its performance compared with a FPGA based image processing algorithm. The results were used to chose a FPGA as the main processing computer of the payload. This concept uses two backside illuminated CMOS image sensor connected to a single FPGA. On the FPGA the developed image processing algorithm should be implemented. Further work is required to realize the proposed concept in building the actual hardware and porting the developed algorithm onto this platform.