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Road and path segmentation in images for autonomous driving scenario
Janíček, Ondřej ; Cihlář, Miloš (referee) ; Svědiroh, Stanislav (advisor)
This bachelor's thesis deals with the topic of segmentation of roads and paths for the purposes of autonomous driving. In the theoretical part, it deals with computer vision, simple segmentation methods, and practical solutions to the problem using convolutional neural networks and classical methods. In the practical part, the work deals with the collection of test data, the selection of a suitable programming language, and the selection of suitable libraries. Subsequently, the procedure for programming our own solution will be presented. Here it starts with pre-processing to convert the image into a grayscale image and filtering the noise, then finding the edges in the image using the Canny edge detector, followed by the definition of the region of interest, with the subsequent Hough transform to detect the straight lines in the image, and in the last stage, filtering the horizontal lines and averaging the remaining lines. At the end of the thesis, the results of the presented solution are compared with respect to robustness and computational complexity.
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