This master thesis deals with the design of a traffic sign detector using the image processing methods. The OpenCV library for working with images in programming language Python is used for this. The first part reports on the using methods. In the next part, these methods were tested on images of traffic signs taken in traffic in different lighting conditions. The results of these tests led to the design of optimal methods and their settings, which were re-verified by verifying on video of driving in traffic. This also revealed the conditions under which they can operate in real-time systems. Finally, an optimization algorithm for compensation of detection errors was proposed from the monitoring of detection waveforms.

This paper deals with traffic signs recognition. Red color area is obtained by thresholding in HSV color model. Selected radiometric deskriptors, Hough transform deskriptors and neural networs are used to classification. In conclusion has been designed complex decision algorithm.

The thesis deals with traffic sign detection and recongnition in the urban environment and outside the town. A precondition for implementation of the system is built-in camera, usually in a car rear-view mirror. The camera scans the scene before the vehicle. The image data are transfered to the connected PC, where the data are transformation to information and evalutations. If the sign was detected the system is visually warned the driver. For a successful goal is divided into four separate blocks. The first part is the preparing of the image data. There are color segmentation with knowledge of color combination traffic signs in Czech Republic. Second part is deals with shape detection in segmentation image. Part number three is deals with recognition of inner pictogram and its finding in the image database. The final part is the visual output of displaying founded traffic signs. The thesis has been prepader so as to ensure detection of all relevant traffic signs in three basic color combinations according to existing by Decree of Ministry of Transport of Czech Republic. The result is the source code for the program MATLAB. .

In the area of Traffic Sign Recognition (TSR), deep learning models are trained to detect and classify images of traffic signs. The amount of data available to train these models is often limited, and collecting more data is time-consuming and expensive. A possible complement to traditional data acquisition, is to generate synthetic images with a generative machine learning model. This thesis investigates the use of denoising diffusion probabilistic models for generating synthetic data of one or multiple traffic sign classes, when providing different amount of real images for that class (classes). In the few-sample method, the number of images used was from 1 to 1000, and zero images were used in the zero-shot method. The results from the few-sample method show that combining synthetic images with real images when training a traffic sign classifier, increases the performance in 3 out of 6 investigated cases. The results indicate that the developed zero-shot method is useful if further refined, and potentially could enable generation of realistic images of signs not seen in the training data.

This study links traffic sign visibility and legibility to quantify the effects of damage or deterioration on sign retroreflective performance. In addition, this study proposes GIS-based data integration strategies to obtain and extract climate, location, and emission data for in-service traffic signs. The proposed data integration strategy can also be used to assess all transportation infrastructures’ physical condition. Additionally, non-parametric machine learning methods are applied to analyze the combined GIS, Mobile LiDAR imaging, and digital photolog big data. The results are presented to identify the most important factors affecting sign visual condition, to predict traffic sign vandalism that obstructs critical messages to drivers, and to determine factors contributing to the temporary obstruction of the sign messages. The results of data analysis provide insight to inform transportation agencies in the development of sign management plans, to identify traffic signs with a higher likelihood of failure, and to schedule sign replacement.

A fast simulated annealing algorithm is developed for automatic object recognition. The normalized correlation coefficient is used as a measure of the match between a hypothesized object and an image. Templates are generated on-line during the search by transforming model images. Simulated annealing reduces the search time by orders of magnitude with respect to an exhaustive search. The algorithm is applied to the problem of how landmarks, for example, traffic signs, can be recognized by an autonomous vehicle or a navigating robot. The algorithm works well in noisy, real-world images of complicated scenes for model images with high information content.

In this thesis, a new algorithm is proposed for the recognition of triangular, circular and rectangular traffic signs and it is implemented on an FPGA platform. The system can recognize 32 different traffic signs with high recognition accuracy. In the proposed method, first the image is segmented into red and blue regions, and according to the area of the each segment, the dominant color is decided. Then, Laplacian of Gaussian (LoG) based edge detection is applied to the segmented image which is followed by Hough Transform for shape extraction. Then, recognition based on Informative Pixel Percentage (IPP) matching is executed on the extracted shapes. The Traffic Sign Recognition (TSR) system is implemented on Virtex 5 FX70T FPGA, which has an embedded PPC440 processor. Some modules of TSR algorithm are designed in the FPGA logic while remaining modules are designed in the PPC440 processor. Work division between FPGA and PPC440 is carried out considering their capabilities and shortcomings of FPGA and processor. Benefits of using an FPGA with an embedded processor are exploited to optimize the system.

I detta arbete utvecklas ett nytt stöldförebyggande skyltsystem för tillfälliga trafikskyltar. Fästet hos det utvecklade skyltsystemet bidrar till att stöld och vandalisering av skyltarna försvåras genom att vara låsande. Viktig trafikinformation kan därför bevaras i trafiken. Det nya skyltsystemet möjliggör montering av flera skyltar på båda sidor av stolpen med steglös höjdjustering.  Skyltsystemet som utvecklats har många av det befintliga systemets fördelar. Det har även fördelar som det befintliga systemet saknar och stor utvecklingspotential. I arbetet utförs även simulationer, beräkningar, hållfasthetstester och användartester för att säkerställa att systemet är säkert att använda i trafiken. / In this project, a new anti-theft sign system is being developed for temporary traffic signs. The connection between the sign and pole of the developed sign system contributes to the theft and vandalism of the signs being made more difficult by being self-locking. Important traffic information can therefore remain in traffic. The new sign system enables mounting of several signs on both sides of the post with stepless height adjustment. The sign system that has been developed has many of the existing systems advantages. It also has advantages that the existing system lacks and great development potential. The project also contains simulations, calculations, strength tests and user tests to ensure that the system is safe to use in traffic.

The purpose of this thesis is to develop the architecture to use the Car2X for observation the local traffic sign and displays it on the OpenStreetMap to provide more information of the road side to the driver. The proposed architecture of this thesis is to convert the traffic sign into the barcode and to be scanned by the barcode scanner and then wirelessly transfers the data to the web server to store the data and displays the traffic sign on the OpenStreetMap in the web browser. It uses two Raspberry Pi boards with CAN-Bus shields for transmitting the data on the CAN-Bus system in the car, a barcode scanner to scan the barcode, a GPS module to get its location, and a WiFi dongle to wirelessly send the data. The thesis also includes the camera to detect the traffic light using OpenCV and sends the GO or STOP command to the car. The results provide the OpenStreetMap with the traffic sign which helps the driver to realize the traffic sign on the road of the desired destination. However, the accuracy of GPS is not satisfied as well as the distance of the barcode scanning, therefore, this thesis suggests that includes the gps position in the barcode and uses the camera to detect the barcode for the improvement in the future.

Computer vision is an area in computer science that attempts to give computers the ability to see and recognise objects using varying sources of input, such as video or pictures. This problem is usually solved by using artificial intelligence (AI) techniques. The most common being deep learning. The project investigates the possibility of using these techniques to recognisetraffic signs in real time. This would make it possible in the future to build a user application that does this. The case study gathers information about available AI techniques, and three object detection deep learning models are selected. These are YOLOv3, SSD, and Faster R-CNN. The chosen models are used in a case study to find out which one is best suited to the task of identifying parking signs in real-time. Faster R-CNN performed the best in terms of recall and precision combined. YOLOv3 slacked behind in recall, but this could be because of how we chose to label the training data. Finally, SSD performed the worst in terms of recall, but was also relatively fast. Evaluation of the case study shows that it is possible to detect parking signs in real time. However, the hardware necessary is more powerful than that offered by currently available mobile platforms. Therefore it is concluded that a cloud solution would be optimal, if the techniques tested were to be implemented in a parking sign reading mobile app. / Datorseende är ett område inom datorvetenskap som fokuserar på att ge maskiner förmågan att se och känna igen objekt med olika typer av input, såsom bilder eller video. Detta är ett problem som ofta löses med hjälp av artificiell intelligens (AI). Mer specifikt, djupinlärning. I detta projekt undersöks möjligheten att använda djupinlärning för att känna igen trafikskyltar i realtid. Detta så att i framtiden kunna bygga en applikation, som kan byggas att känna igen parkeringsskyltar i realtid. Fallstudien samlar information om tillgängliga AI-tekniker, och tre djupinlärningsmodeller väljs ut. Dessa är YOLOv, SSD, och Faster R-CNN. Dessa modeller används i en fallstudie för att ta reda på vilken av dem som är bäst lämpad för uppgiften att känna igen parkeringsskyltar i realtid. Faster R-CNN presterade bäst vad gäller upptäckande av objekt och precision tillsammans. YOLOv3 upptäckte färre object, men det är sannolikt att detta berodde på hur vi valde att markera träningsdatan. Slutligen upptäckte SSD minst antal objekt, men presterade också relativt snabbt. Bedömning av fallstudien visar att det är möjligt att känna igen parkeringsskyltar i realtid. Den nödvändiga hårdvaran är dock kraftfullare än den som erbjuds av mobiler för närvarande. Därför dras slutsatsen att en molnlösning skulle vara optimal, om de testade teknikerna skulle användas för att implementera en app för att känna igen parkeringskyltar.