This project aims to develop an automated image pre-processing chain to extract valuable information from appliance labels before recycling. The primary goal is to improve optical character recognition accuracy by addressing noise issues using reinforcement learning and an evolutionary algorithm. Python was selected as the primary programming language for this project due to its extensive support for machine learning and computer vision libraries. Different techniques are implemented to enhance text extraction from labels. Binary Robust Invariant Scalable Keypoints (BRISK) are used to straighten labels and separate the label from the background. You Only Look Once version 8x (YOLOv8x) is then used for extracting the regions containing the text of interest. The reinforcement learning model and genetic algorithm dataset are created using BRISK with YOLOv8x. The results showed that pre-processing images in the dataset, provided through BRISK and YOLOv8x, does not affect text extraction accuracy, as suggested by reinforcement learning and evolutionary algorithms. / Detta projekt syftar till att utveckla en automatiserad bildförbehandlingskedja för att extrahera värdefull information från apparatmärken före återvinning. Det primära målet är att förbättra noggrannheten för optisk teckenigenkänning genom att hantera brusproblem med hjälp av förstärkningsinlärning och en evolutionär algoritm. Python valdes som det primära programmeringsspråket för detta projekt på grund av dess omfattande stöd för maskininlärnings- och datorseendebibliotek. Olika tekniker implementeras för att förbättra textutvinningen från etiketterna. Binary Robust Invariant Scalable Keypoints (BRISK) används för att räta ut etiketter och separera etiketten från bakgrunden. You Only Look Once version 8x (YOLOv8x) används sedan för att extrahera områden som innehåller den önskade texten. Datasetet för förstärkningsinlärningsmodellen och den genetiska algoritmen skapas genom att använda BRISK med YOLOv8x. Resultaten visade att förbehandlingen av bilder i datasetet, som tillhandahålls genom BRISK och YOLOv8x, inte påverkar noggrannheten för textutvinning, som föreslagits av förstärkningsinlärning och evolutionära algoritmer.

Hydropower plants create blockages in fish migration routes. Fish ladders can serve as alternative routes but are complex to install and follow up to help adapt and develop them further. In this study, computer vision tools are considered in this regard. More specifically, object detection is applied to images collected in a hydropower plant fish ladder to localise and classify wild, farmed and unknown fish labelled according to the presence, absence or uncertainty of an adipose fin. Fish migration patterns are not deterministic, making it a challenge to collect representative and balanced data to train a model that is resilient to changing conditions. In this study, two data imbalances are addressed by modifying a YOLOv3 baseline model: foreground-foreground class imbalance is targeted using hard and soft resampling and positional imbalance using translation augmentation. YOLOv3 is a convolutional neural network predicting bounding box coordinates, class probabilities and confidence scores simultaneously. It divides images into grids and makes predictions based on grid cell locations and anchor box offsets. Performance is estimated across 10 random data splits and different bounding box overlap thresholds, using (mean) average precision as well as recall, precision and F1 score estimated at optimal validation set confidence thresholds. The Wilcoxon signed-ranks test is used for determining statistical significance. In experiments, the best performance was observed on wild and farmed fish, with F1 scores reaching 94.8 and 89.0 percent respectively. The inconsistent appearance of unknown fish appears harder to generalise to, with a corresponding F1 score of 65.7 percent. Soft sampling but especially translation augmentation contributed to enhanced performance and reduced variance, implying that the baseline model is particularly sensitive to positional imbalance. Spatial dependencies introduced by YOLOv3’s grid cell strategy likely produce local bias or overfitting. An experimental evaluation highlight the importance of not relying on a single data split when evaluating performance on a moderately large or custom dataset. A key challenge observed in experiments is the choice of a suitable confidence threshold, influencing the dynamics of the results. / Vattenkraftverk blockerar fiskars vandringsvägar. Fisktrappor kan skapa alternativa vägar men är komplexa att installera och följa upp för vidare anpassning och utveckling. I denna studie betraktas datorseende i detta avseende. Mer specifikt appliceras objektdetektering på bilder samlade i en fisktrappa i anslutning till ett vattenkraftverk, med målet att lokalisera och klassificera vilda, odlade och okända fiskar baserat på förekomsten, avsaknaden eller osäkerheten av en fett-fena. Fiskars migrationsmönster är inte deterministiska vilket gör det svårt att samla representativ och balanserad data för att trana en modell som kan hantera förändrade förutsättningar. I denna studie addresseras två obalanser i datan genom modifikation av en YOLOv3 baslinjemodell: klass-obalans genom hård och mjuk återanvändning av data och positionell obalans genom translation av bilder innan träning. YOLOv3 är ett convolutional neural network som simultant förutsäger avgränsnings-lådor, klass-sannolikheter och prediktions-säkerhet. Bilder delas upp i rutnätceller och prediktioner görs baserat på cellers position samt modifikation av fördefinierade avgränsningslådor. Resultat beräknas på 10 slumpmässiga uppdelningar av datan och för olika tröskelvärden för avgränsningslådors överlappning. På detta beräknas (mean) average precision, liksom recall, precision och F1 score med tröskelvärden för prediktions-säkerhet beräknat på valideringsdata. Wilcoxon signed-ranks test används för att avgöra statistisk signifikans. Bäst resultat observeras på vilda och odlade fiskar, med F1 scores som når 94.8 respektive 89.0 procent. Okända fiskars inkonsekventa utseenden verkar svårare att generalisera till, med en motsvarande F1 score på 65.7 procent. Mjuk återanvändning av data men speciellt translation bidrar till förbättrad prestanda och minskad varians, vilket pekar på att baslinjemodellen är särskilt känslig för positionell obalans. Spatiala beroenden skapade av YOLOv3s rutnäts-strategi producerar troligen lokal partiskhet eller överträning. I en experimentell utvärdering understryks vikten av multipel uppdelning av datan vid evaluering på ett måttligt stort eller egenskapat dataset. Att välja tröskelvärdet för prediktions-säkerhet anses utmanande och påverkar resultatens dynamik.

Основная цель выпускной квалификационной работы состоит в разработке системы компьютерного зрения для определения вида фракции щебня. А также определении наиболее эффективного метода для определения фракции щебня, сравнивая задачи компьютерного зрения: обнаружение объектов и классификация. Первая часть исследования посвящена анализу существующий методов и алгоритмов классификации изображений на основе нейронных сетей. Были проанализированы модели, предназначенные для обнаружения объектов и классификации. Для задачи классификации изображений сравнение выполнялось для моделей: Resnet, Efficientnet, Deit, Tinyvit. Для задачи обнаружения объектов: Yolo, Faster R-CNN и SSD. Во второй части исследования была обучена модель обнаружения объектов и обучены модели классификации. После произведено сравнение производительности данных моделей для решаемой задачи – определения фракции щебня. Третья часть выпускной квалификационной работы направлена на разработку системы компьютерного зрения для определения фракции щебня. Для работоспособности системы было развернуто два Docker-контейнера и сервер Uvicorn с работающим приложением FastAPI. / The main objective of the final qualification work is to develop a computer vision system for determining the type of crushed stone fraction. As well as determining the most effective method for determining the crushed stone fraction, comparing the tasks of computer vision: object detection and classification. The first part of the study is devoted to the analysis of existing methods and algorithms for image classification based on neural networks. Models designed for object detection and classification were analyzed. For the task of image classification, the comparison was performed for the following models: Resnet, Efficientnet, Deit, Tinyvit. For the task of object detection: Yolo, Faster R-CNN and SSD. In the second part of the study, an object detection model was trained and classification models were trained. After that, a comparison of the performance of these models for the problem being solved - determining the crushed stone fraction was made. The third part of the final qualification work is aimed at developing a computer vision system for determining the crushed stone fraction. For the system to work, two Docker containers and a Uvicorn server with a running FastAPI application were deployed.

Research in the Arctic is of ever growing importance, and modern technology is used in news ways to map and understand this very complex region and how it is effected by climate change. Here, animals and vegetation are tightly coupled with their environment in a fragile ecosystem, and when the environment undergo rapid changes it risks damaging these ecosystems severely.  Understanding what kind of data that has potential to be used in artificial intelligence, can be of importance as many research stations have data archives from decades of work in the Arctic. In this thesis, a YOLOv4 object detection model has been trained on two classes of images to investigate the performance impacts of disturbances in the training data set. An expanded data set was created by augmenting the initial data to contain various disturbances. A model was successfully trained on the augmented data set and a correlation between worse performance and presence of noise was detected, but changes in saturation and altered colour levels seemed to have less impact than expected. Reducing noise in gathered data is seemingly of greater importance than enhancing images with lacking colour levels. Further investigations with a larger and more thoroughly processed data set is required to gain a clearer picture of the impact of the various disturbances.

The study of this research has a challenge of learning data gathering sensor programming and design of electronic sensor circuit. The cost of autonomous vehicle development is expensive compared to purchasing an economy vehicle such as the Hyundai Elantra. Keeping the development cost down is critical to maintaining a competitive edge on vehicle pricing with newer technologies. Autonomous vehicle sensor integration was designed and then tested for the driving vision data-gathering system that requires the system to gather driving vision data utilizing area scan sensors, Lidar, ultrasonic sensor, and camera on real road scenarios. The project utilized sensors such as cheap cost LIDAR, which is that drone is used for on the road testing; other sensors include myRIO (myRIO Hardware), LabVIEW (LabVIEW software), LIDAR-Lite v3 (Garmin, 2019), Ultrasonic sensor, and Wantai stepper motor (Polifka, 2020). This research helps to reduce the price of usage of autonomous vehicle driving systems in the city. Due to resolution and Lidar detecting distance, the test environment is limited to within city areas. Lidar is the most expensive equipment on autonomous vehicle driving data gathering systems. This study focuses on replacing expensive Lidar, ultrasonic sensor, and camera to drone scale low-cost Lidar to real size vehicle. With this study, economic expense autonomous vehicle driving data acquisition is possible. Lowering the price of autonomous vehicle driving data acquisition increases involving new companies on the autonomous vehicle market. Multiple testing with multiple cars is possible. Since multiple testing at the same time is possible, collecting time reduces.

Developing the greenest battery cell and establishing a European supply of batteries is the main goal for Northvolt. To achieve this, the recycling of batteries is a key enabler towards closing the loop and enabling the future of energy.When it comes to the recycling of electric vehicle battery packs, dismantling is one of of the main process steps.Given the size, weight and high voltage of the battery packs, automatic disassembly using robots is the preferred solution. The work presented in this thesis aims to develop and integrate a vision system able to identify and verify the battery pack dismantling process. To achieve this, two cameras were placed in the robot cell and the object detectors You Only Look Once (YOLO) and template matching were implemented, tested and compared. The results show that YOLO is the best object detector out of the ones implemented. The integration of the vision system with the robot controller was also tested and showed that with the results from the vision system, the robot controller can make informed decisions regarding the disassembly.

I detta arbete genomförs utvecklingen av ett realtids objektdetekteringssystem för iOS. För detta ändamål används YOLO, en ett-stegs objektdetekterare och ett s.k. ihoplänkat neuralt nätverk vilket åstadkommer betydligt bättre prestanda än övriga realtidsdetek- terare i termer av hastighet och precision. En dörrdetekterare baserad på YOLO tränas och implementeras i en systemutvecklingsprocess. Maskininlärningsprocessen sammanfat- tas och praxis för att undvika överträning eller “overfitting” samt för att öka precision och hastighet diskuteras och appliceras. Vidare genomförs en rad experiment vilka pekar på att dataaugmentation och inkludering av negativ data i ett dataset medför ökad precision. Hyperparameteroptimisering och kunskapsöverföring pekas även ut som medel för att öka en objektdetekringsmodells prestanda. Författaren lyckas öka modellens mAP, ett sätt att mäta precision för objektdetekterare, från 63.76% till 86.73% utifrån de erfarenheter som dras av experimenten. En modells tendens för överträning utforskas även med resultat som pekar på att träning med över 300 epoker rimligen orsakar en övertränad modell. / This paper features the creation of a real time object detection system for mobile iOS using YOLO, a state-of-the-art one stage object detector and convoluted neural network far surpassing other real time object detectors in speed and accuracy. In this process an object detecting model is trained to detect doors. The machine learning process is outlined and practices to combat overfitting and increasing accuracy and speed are discussed. A series of experiments are conducted, the results of which suggests that data augmentation, including negative data in a dataset, hyperparameter optimisation and transfer learning are viable techniques in improving the performance of an object detection model. The author is able to increase mAP, a measurement of accuracy for object detectors, from 63.76% to 86.73% based on the results of experiments. The tendency for overfitting is also explored and results suggest that training beyond 300 epochs is likely to produce an overfitted model.

This thesis investigates the impact of image enhancement techniques on object detection for carsin real-world traffic scenarios. The study focuses on upscaling and light correction treatments andtheir effects on detecting cars in challenging conditions. Initially, a YOLOv8x model is trained on clear static car images. The model is then evaluated on a test dataset captured in real-world driving with images from a front-mounted camera on a car, incorporating various lighting conditions and challenges. The images are then enhanced with said treatments and then evaluated again. The results in this experiment with its specific context show that upscaling seems to decreasemAP performance while lighting correction slightly improves accuracy. Additional training on acomplex image dataset outperforms all other approaches, highlighting the importance of diverse and realistic training data. These findings contribute to advancing computer vision research for object detection models.

Defects on fiber products have varied appearances and are common in production lines. A reliable system that can classify and identify defects without subjectivity and fatigue can improve a company's quality management. Computer vision systems are crucial for any autonomous system, but accuracy is essential for real-life applications. This study aims to investigate the contribution of computer vision through computer vision and artificial intelligence in detecting defects in formed fiber products. A hand-crafted dataset of four common defects from the production line was created and tested using transfer learning. The system's performance was measured in terms of mean average precision (mAP), precision, and recall, resulting in a performance of 81.8% mAP, 0.84 recall rate, and 0.79 precision rate for the hand-crafted dataset. / Defekter på fiberprodukter har olika framträdanden och är vanliga i produktionslinjer. Ett tillförlitligt system som kan klassificera och identifiera defekter utan subjektivitet och trötthet kan förbättra ett företags kvalitetsledning. Ett datorseende-system är avgörande för alla autonoma system, men noggrannhet är viktigt för tillämpningar i verkliga livet. Denna studie syftar till att undersöka bidraget från datorseende genom datorseende och artificiell intelligens för att upptäcka defekter i formade fiberprodukter. Ett handgjort dataset med fyra vanliga defekter från produktionslinjen skapades och testades med transfer learning. Systemets prestanda mättes i termer av medelvärde av genomsnittlig precision (mAP), precision och återkallelse, vilket resulterade i en prestanda på 81,8% mAP, 0,84 återkallningsfrekvens och 0,79 precision frekvens för det handgjorda datasetet.

Background. This Thesis addresses the critical issue of disease management in ricecrops, a key factor in ensuring both food security and the livelihoods of farmers. Objectives. The primary focus of this research is to tackle the often-overlooked challenge of precise disease localization within rice plants by harnessing the power of deep learning techniques. The primary goal is not only to classify diseases accurately but also to pinpoint their exact locations, a vital aspect of effective disease management. The research encompasses early disease detection, classification, andthe precise identification of disease locations, all of which are crucial components of a comprehensive disease management strategy. Methods. To establish the reliability of the proposed model, a rigorous validation process is conducted using standardized datasets of rice plant diseases. Two fundamental research questions guide this study: (1) Can deep learning effectively achieve early disease detection, accurate disease classification, and precise localizationof rice plant diseases, especially in scenarios involving multiple diseases? (2) Which deep learning architecture demonstrates the highest level of accuracy in both disease  diagnosis and localization? The performance of the model is evaluated through the application of three deep learning architectures: Masked RCNN, YOLO V8, and SegFormer. Results. These models are assessed based on their training and validation accuracy and loss, with specific metrics as follows: For Masked RCNN, the model achieves a training accuracy of 91.25% and a validation accuracy of 87.80%, with corresponding training and validation losses of 0.3215 and 0.4426. YOLO V8 demonstrates a training accuracy of 85.50% and a validation accuracy of 80.20%, with training andvalidation losses of 0.4212 and 0.5623, respectively. SegFormer shows a training accuracy of 78.75% and a validation accuracy of 75.30%, with training and validation losses of 0.5678 and 0.6741, respectively. Conclusions. This research significantly contributes to the field of agricultural disease management, offering valuable insights that have the potential to enhance crop yield, food security, and the overall well-being of farmers