Every year, drivers are spending over 3 billions to repair damage on vehicle caused by potholes. Along with the financial disaster, potholes cause frustration in drivers. Also, with the emerging development of automated vehicles, road safety with automation in mind is being a necessity. Deep Learning techniques offer intelligent alternatives to reduce the loss caused by spotting pothole. The world is connected in such a way that the information can be shared in no time. Using the power of connectivity, we can communicate the information of potholes to other vehicles and also the department of Transportation for necessary action. A significant number of research efforts have been done with a view to help detect potholes in the pavements. In this thesis, we have compared two object detection algorithms belonging to two major classes i.e. single shot detectors and two stage detectors using our dataset. Comparing the results in the Faster RCNN and YOLOv5, we concluded that, potholes take a small portion in image which makes potholes detection with YOLOv5 less accurate than the Faster RCNN, but keeping the speed of detection in mind, we have suggested that YOLOv5 will be a better solution for this task. Using the YOLOv5 model and image processing technique, we calculated approximate area of potholes and visualized the shape of potholes. Thus obtained information can be used by the Department of Transportation for planning necessary construction tasks. Also, we can use these information to warn the drivers about the severity of potholes depending upon the shape and area.

Different type of images provided by various combinations of cameras have the power to help increase and optimize plant growth. Along with a powerful deep learning model, for the purpose of detecting these stress indicators in RGB images, can significantly increase the harvest yield. The field of AI solutions in agriculture is not vastly explored and this thesis aims to take a first step in helping explore different techniques to detect early plant stress. Within this work, different types and combinations of camera modules will initially be reviewed and evaluated based on the amount of information they provide. Using the chosen cameras, we manually set up datasets and annotations, chose and then trained a suitable and appropriate algorithm to predict deviations from an ideal state in plant production. The algorithm chosen was Faster RCNN, which resulted in having a very high detection accuracy. Along with the main type of cameras, a new particular type of images analysis, named SI-NDVI, is done using a particular combination of the main three cameras and the results show that it is able to detect vegetation and able to predict or show if a plant is stressed or not. An in-depth research is done on all these techniques to create a good quality dataset for the purpose of early stress detection. / Olika typer av bilder försedda av olika kombinationer av kameror har kapaciteten att hjälpa öka och optimera odling av växter. Tillsammans med en kraftfull deep learning-modell, för att detektera olika stressindikatorer i RGB bilder, kan signifikant öka skördar. Fältet av AI-lösningar inom jordbruk är inte väl utforskat och denna uppsats siktar på att ta ett första steg i utforskandet av olika tekniker för att detektera tidig stress hos växter. Inom detta arbete kommer olika typer och kombinationer av kameramoduler bli utvärderade baserat på hur mycket information de kan förse. Genom att använda de valda kamerorna skapar vi själva dataseten och kategoriserar dem, därefter välja och träna en lämplig algoritm för att förutspå förändringar från ett idealt tillstånd för växtens tillväxt. Algoritmen som valdes var Faster RCNN, vilken hade en väldigt hög träffsäkerhet. Parallellt med de huvudsakliga kamerorna genomförs en ny typ av bildanalys vid namn SI-NDVI genom användandet av en särskild kombination av de tre kameror och resultat visar att det är möjligt att detektera vegetation och förutspå eller visa om en växt är stressad eller inte. En fördjupad undersökning genomförs på alla dessa tekniker för att skapa ett dataset av god kvalité för att kunna förutspå tidig stress.

The goal of this thesis is to design and implement algorithm for text layout analysis in historical documents. Neural network was used to solve this problem, specifically architecture Faster-RCNN. Dataset of 6 135 images with historical newspaper was used for training and testing. For purpose of the thesis four models of neural networks were trained: model for detection of words, headings, text regions and model for words detection based on position in line. Outputs from these models were processed in order to determine text layout in input image. A modified F-score metric was used for the evaluation. Based on this metric, the algorithm reached an accuracy almost 80 %.

Enhanced vision and object detection could be useful in the aviation domain in situations of bad weather or cluttered environments. In particular, enhanced vision and object detection could improve situational awareness and aid the pilot in environment interpretation and detection of hazardous objects. The fundamental concept of object detection is to interpret what objects are present in an image with the aid of a prediction model or other feature extraction techniques. Constructing a comprehensive data set that can describe the operational environment and be robust for weather and lighting conditions is vital if the object detector is to be utilised in the avionics domain. Evaluating the accuracy and robustness of the constructed data set is crucial. Since erroneous detection, referring to the object detection algorithm failing to detect a potentially hazardous object or falsely detecting an object, is a major safety issue. Bayesian uncertainty estimations are evaluated to examine if they can be utilised to detect miss-classifications, enabling the use of a Bayesian Neural Network with the object detector to identify an erroneous detection. The object detector Faster RCNN with ResNet-50-FPN was utilised using the development framework Detectron2; the accuracy of the object detection algorithm was evaluated based on obtained MS-COCO metrics. The setup achieved a 50.327 % AP@[IoU=.5:.95] score. With an 18.1 % decrease when exposed to weather and lighting conditions. By inducing artificial artefacts and augmentations of luminance, motion, and weather to the images of the training set, the AP@[IoU=.5:.95] score increased by 15.6 %. The inducement improved the robustness necessary to maintain the accuracy when exposed to variations of environmental conditions, which resulted in just a 2.6 % decrease from the initial accuracy. To fully conclude that the augmentations provide the necessary robustness for variations in environmental conditions, the model needs to be subjected to actual image representations of the operational environment with different weather and lighting phenomena. Bayesian uncertainty estimations show great promise in providing additional information to interpret objects in the operational environment correctly. Further research is needed to conclude if uncertainty estimations can provide necessary information to detect erroneous predictions.