In recent years the wide availability of high-resolution satellite images has made the remote monitoring of water resources all over the world possible. While the detection of open water from satellite images is relatively easy, a significant percentage of the water extent of wetlands is covered by vegetation. Convolutional Neural Networks have shown great success in the task of detecting wetlands in satellite images. However, these models require large amounts of manually annotated satellite images, which are slow and expensive to produce. In this paper we use self-supervised training methods to train a Convolutional Neural Network to detect water from satellite images without the use of annotated data. We use a combination of deep clustering and negative sampling based on the paper ”Unsupervised Single-Scene Semantic Segmentation for Earth Observation”, and we expand the paper by changing the clustering loss, the model architecture and implementing an ensemble model. Our final ensemble of self-supervised models outperforms a single supervised model, showing the power of self-supervision. / Under de senaste åren har den breda tillgången på högupplösta satellitbilder möjliggjort fjärrövervakning av vattenresurser över hela världen. Även om det är relativt enkelt att upptäcka öppet vatten från satellitbilder, täcks en betydande andel av våtmarkernas vattenutbredning av vegetation. Lyckligtvis kan radarsignaler tränga igenom vegetation, vilket gör det möjligt för oss att upptäcka vatten gömt under vegetation från satellitradarbilder. Under de senaste åren har Convolutional Neural Networks visat stor framgång i denna uppgift. Tyvärr kräver dessa modeller stora mängder manuellt annoterade satellitbilder, vilket är långsamt och dyrt att producera. Självövervakad inlärning är ett område inom maskininlärning som syftar till att träna modeller utan användning av annoterade data. I den här artikeln använder vi självövervakad träningsmetoder för att träna en Convolutional Neural Network-baserad modell för att detektera vatten från satellitbilder utan användning av annoterade data. Vi använder en kombination av djup klustring och kontrastivt lärande baserat på artikeln ”Unsupervised Single-Scene Semantic Segmentation for Earth Observation”. Dessutom utökar vi uppsatsen genom att modifiera klustringsförlusten och modellarkitekturen som används. Efter att ha observerat hög varians i våra modellers prestanda implementerade vi också en ensemblevariant av vår modell för att få mer konsekventa resultat. Vår slutliga ensemble av självövervakade modeller överträffar en enda övervakad modell, vilket visar kraften i självövervakning.

Stena Recycling is one of the leading recycling companies in Sweden and at their facility in Halmstad, 300 tonnes of refuse are handled every day where aluminium is one of the most valuable materials they sort. Today, most of the sorting process is done automatically, but there are still parts of the refuse that are not correctly sorted. Approximately 4\% of the aluminium is currently not properly sorted and goes to waste. Earlier works have investigated using machine vision to help in the sorting process at Stena Recycling. However, consistently through all these previous works, there is a problem in gathering enough annotated data to train the machine learning models. This thesis aims to investigate how machine vision could be used in the recycling process and if pre-training models using self-supervised learning can alleviate the problem of gathering annotated data and yield an improvement. The results show that machine vision models could viably be used in an information system to assist operators. This thesis also shows that pre-training models with self-supervised learning may yield a small increase in performance. Furthermore, we show that models pre-trained using self-supervised learning also appear to transfer the knowledge learned from images created in a lab environment to images taken at the recycling plant.

Deep learning has shown to be successful on the task of semantic segmentation of three-dimensional (3D) point clouds, which has many interesting use cases in areas such as autonomous driving and defense applications. A common type of sensor used for collecting 3D point cloud data is Light Detection and Ranging (LiDAR) sensors. In this thesis, a time-correlated single-photon counting (TCSPC) LiDAR is used, which produces very accurate measurements over long distances up to several kilometers. The dataset collected by the TCSPC LiDAR used in the thesis contains two classes, person and other, and it comes with several challenges due to it being limited in terms of size and variation, as well as being extremely class imbalanced. The thesis aims to identify, analyze, and evaluate state-of-the-art deep learning models for semantic segmentation of point clouds produced by the TCSPC sensor. This is achieved by investigating different loss functions, data variations, and data augmentation techniques for a selected state-of-the-art deep learning architecture. The results showed that loss functions tailored for extremely imbalanced datasets performed the best with regard to the metric mean intersection over union (mIoU). Furthermore, an improvement in mIoU could be observed when some combinations of data augmentation techniques were employed. In general, the performance of the models varied heavily, with some achieving promising results and others achieving much worse results.

Light Detection and Ranging (LiDAR) sensors have many different application areas, from revealing archaeological structures to aiding navigation of vehicles. However, it is challenging to interpret and fully use the vast amount of unstructured data that LiDARs collect. Automatic classification of LiDAR data would ease the utilization, whether it is for examining structures or aiding vehicles. In recent years, there have been many advances in deep learning for semantic segmentation of automotive LiDAR data, but there is less research on aerial LiDAR data. This thesis investigates the current state-of-the-art deep learning architectures, and how well they perform on LiDAR data acquired by an Unmanned Aerial Vehicle (UAV). It also investigates different training techniques for class imbalanced and limited datasets, which are common challenges for semantic segmentation networks. Lastly, this thesis investigates if pre-training can improve the performance of the models. The LiDAR scans were first projected to range images and then a fully convolutional semantic segmentation network was used. Three different training techniques were evaluated: weighted sampling, data augmentation, and grouping of classes. No improvement was observed by the weighted sampling, neither did grouping of classes have a substantial effect on the performance. Pre-training on the large public dataset SemanticKITTI resulted in a small performance improvement, but the data augmentation seemed to have the largest positive impact. The mIoU of the best model, which was trained with data augmentation, was 63.7% and it performed very well on the classes Ground, Vegetation, and Vehicle. The other classes in the UAV dataset, Person and Structure, had very little data and were challenging for most models to classify correctly. In general, the models trained on UAV data performed similarly as the state-of-the-art models trained on automotive data.

This project is about screw hole detection using neural networks for automated assembly and disassembly. In a lot of industrial companies, such as Ericsson AB, there are products such as radio units or filters that have a lot of screw holes. Thus, the assembly and disassemble process is very time consuming and demanding for a human to assemble and disassemble the products. The problem statement in this project is to investigate the performance of neural networks within object detection and semantic segmentation to detect screw holes in industrial products. Different industrial models were created and synthetic data was generated in Blender. Two types of experiments were done, the first one compared an object detection algorithm (Faster R-CNN) with a semantic segmentation algorithm (SegNet) to see which area is most suitable for hole detection. The results showed that semantic segmentation outperforms object detection when it comes to detect multiple small holes. The second experiment was to further investigate about semantic segmentation algorithms by adding U-Net, PSPNet and LinkNet into the comparison. The networks U-Net and LinkNet were the most successful ones and achieved a Mean Intersection over Union (MIoU) of around 0.9, which shows that they have potential for further development. Thus, conclusions draw in this project are that segmentation algorithms are more suitable for hole detection than object detection algorithms. Furthermore, it shows that there is potential in neural networks within semantic segmentation to detect screw holes because of the results of U-Net and LinkNet. Future work that one can do is to create more advanced product models, investigate other segmentation networks and hyperparameter tuning. / Det här projektet handlar om skruvhålsdetektering genom att använda neurala nätverk för automatiserad montering och demontering. I många industriföretag, såsom Ericsson AB, finns det många produkter som radioenheter eller filter som har många skruvhål. Därmed, är monterings - och demonteringsprocessen väldigt tidsfördröjande och krävande för en människa att montera och demontera produkterna. Problemformuleringen i detta projekt är att undersöka prestationen av olika neurala nätverk inom objekt detektering och semantisk segmentering för skurvhålsdetektering på indutriella produkter. Olika indutriella modeller var skapade och syntetisk data var genererat i Blender. Två typer av experiment gjordes, den första jämförde en objekt detekterings algoritm (Faster R-CNN) med en semantisk segmenterigs algoritm för att vilket område som är mest lämplig för hål detektering. Resultaten visade att semantisk segmentering utpresterar objekt detektering när det kommer till att detektera flera små hål. Det andra experimentet handlade om att vidare undersöka semantiska segmenterings algoritmer genom att addera U-Net, PSPNet och LinkNet till jämförelsen. Nätverken U-Net och PSPNet var de mest framgångsrika och uppnåde en Mean Intersection over Union (MIoU) på cirka 0.9, vilket visar på att de har potential för vidare utveckling. Slutsatserna inom detta projekt är att semantisk segmentering är mer lämplig för hål detektering än objekt detektering. Dessutom, visade sig att det finns potential i neurala nätverk inom semantisk segmentering för att detejtera skruvhål på grund av resultaten av U-Net och LinkNet. Framtida arbete som man kan göra är att skapa flera avancerade produkt modeller, undersöka andra segmenterisk nätverk och hyperparameter tuning.