1) real_time_object_detection.py: Python script for deploying trained deep neural network in live stream.<br>2) augmentation.py: Python script for augmenting Detector images.<div>3) tcp_send_command.py: Python script for sending system stop CPI command to Gateway as a CPI message.</div>

Quality control is an important part of any production line. It can be done manually but is most efficient if automated. Inspecting qualitycan include many different processes but this thesisisfocusedon the visual inspection for cracks and scratches. The best way of doingthis at the time of writing is with the help of Artificial Intelligence (AI), more specifically Deep Learning (DL).However, these need a training datasetbeforehand to train on and for some smaller companies, this mightnotbean option. This study triesto find an alternative visual inspection method,that does notrelyon atrained deep learning modelfor when trainingdata is severely limited. Our method is to use edge detection algorithmsin combination with a template to find any edge that doesn’t belong. These include scratches, cracks, or misaligned stickers. These anomalies arethen highlighted in the original picture to show where the defect is. Since deep learningis stateof the art ofvisual inspection, it is expected to outperform template matching when sufficiently trained.To find where this occurs,the accuracy of template matching iscompared to the accuracy of adeep learning modelat different training levels. The deep learning modelisto be trained onimage augmenteddatasets of size: 6, 12, 24, 48, 84, 126, 180, 210, 315, and 423. Both template matching and the deep learning modelwas tested on the samebalanceddataset of size 216. Half of the dataset was images of scratched units,and the other half was of unscratched units. This gave a baseline of 50% where anything under would be worse thanjust guessing. Template matching achieved an accuracy of 88%, and the deep learning modelaccuracyrose from 51% to 100%as the training setincreased. This makes template matching have better accuracy then AI trained on dataset of 84imagesor smaller. But a deep learning modeltrained on 126 images doesstart to outperform template matching. Template matching did perform well where no data was available and training adeep learning modelis no option. But unlike a deep learning model, template matching would not need retraining to find other kinds of surface defects. Template matching could also be used to find for example, misplaced stickers. Due to the use of a template, any edge that doesnot match isdetected.  The ways to train deep learning modelis highly customizable to the users need. Due to resourceand knowledge restrictions, a deep dive into this subject was not conducted.For template matching, only Canny edge detection was used whenmeasuringaccuracy. Other edge detection methodssuch as, Sobel, and Prewitt was ruledoutearlier in this study.

The 2019 novel coronavirus has been proven to present several unique features on chest X-rays and CT-scans that distinguish it from imaging of other pulmonary diseases such as bacterial pneumonia and viral pneumonia unrelated to COVID-19. However, the key characteristics of a COVID-19 infection have been proven challenging to detect with the human eye. The aim of this project is to explore if it is possible to distinguish a patient with COVID-19 from a patient who is not suffering from the disease from posteroanterior chest X-ray images using synthetic minority over-sampling and transfer learning. Furthermore, the report will also present the mechanics of COVID-19, the used dataset and models and the validity of the results.

Accurate measurement brain region volumes are important in studying brain plasticity, which brings insight into the fundamental mechanisms in animal, memory, cognitive, and behavior research. The traditional methods of brain volume measurements are ellipsoid or histology. In this study, micro-computed tomography (micro-CT) method was used to achieve more accurate results. However, manual segmentation of micro-CT images is time consuming, hard to reprodu-ce, and has the risk of human error. Automatic image segmentation is a faster method for obtaining the segmentations and has the potential to provide eciency, reliability, repeatability, and scalability. Different methods are tested and compared in this thesis. In this project, 29 micro-CT scans of lizard heads were used and measurements of the volumes of 6 dierent brain regions was of interest. The lizard heads were semi-manually segmented into 6 regions and three open-source segmentation algorithms were compared, one atlas-based algorithm and two deep-learning-based algorithms. Dierent number of training data were quantitatively compared for deep-learning methods from all three orientations (sagittal, horizontal and coronal). Data augmentation was tested and compared, as well. The comparison shows that the deep-learning algorithms provided more accurate results than the atlas-based algorithm. The results also demonstrated that in the sagittal plane, 5 manually segmented images for training are enough to provide resulting predictions with high accuracy (dice score 0.948). Image augmentation was shown to improve the accuracy of the segmentations but a unique dataset still plays an important role. In conclusion, the results show that the manual segmentation work can be reduced drastically by using deep learning for image segmentation. / Noggrann mätning av hjärnregionsvolymer är viktigt för att studera hjärnans plasticitet, vilket ger insikt i de grundläggande mekanismerna inom djurstudier, minnes-, kognitions- och beteendeforskning. De traditionella metoderna för mätning av hjärnvolym är ellipsoid modellen eller histologi. I den här studien användes mikrodatortomografi (mikro-CT) metoden för att få mer korrekta resultat. Manuell segmentering av mikro-CT-bilder är dock tidskrävande, svår att reproducera och har en risk för mänskliga fel. Automatisk bildsegmentering är en snabb metod för att erhålla segmenteringarna. Den har potentialen att ge eektivitet, tillförlitlighet, repeterbarhet och skalbarhet. Därför testas och jämförs tre metoder för automatisk segmentering i denna studie. I projektet användes 29 mikro-CT-bilder av ödlehuvuden för att få fram volymerna hos 6 olika hjärnregioner. Ödlehuvudena segmenterades halvmanu- ellt i 6 regioner och tre segmenteringsalgoritmer med öppen källkod jämfördes (en atlasbaserad algoritm och två djupinlärningsbaserade algoritmer). Olika antal träningsdata jämfördes kvantitativt för djupinlärningsmetoder i alla tre plan (sagittal, horisontell och frontal). Även datautökning testades och analyserades. Jämförelsen visar att djupinlärningsalgoritmerna gav mer signifikanta resultat än den atlasbaserade algoritmen. Resultaten visade även att i det sagittala planet räcker det med 5 manuellt segmenterade bilder för träning för att ge segmenteringar med hög noggrannhet (dice värde 0,948). Datautökningen har visat sig förbättra segmenteringarnas noggrannhet, men ett unikt dataset spelar fortfarande en viktig roll. Sammanfattningsvis visar resultaten att det manuella segmenteringsarbetet kan minskas drastiskt genom att använda djupinlärning för bildsegmentering.