Thesis Details
Detekce objektů v laserových skenech pomocí konvolučních neuronových sítí
This thesis is aimed at detection of lines of horizontal road markings from a point cloud, which was obtained using mobile laser mapping. The system works interactively in cooperation with user, which marks the beginning of the traffic line. The program gradually detects the remaining parts of the traffic line and creates its vector representation. Initially, a point cloud is projected into a horizontal plane, crating a 2D image that is segmented by a U-Net convolutional neural network. Segmentation marks one traffic line. Segmentation is converted to a polyline, which can be used in a geo-information system. During testing, the U-Net achieved a segmentation accuracy of 98.8\%, a specificity of 99.5\% and a sensitivity of 72.9\%. The estimated polyline reached an average deviation of 1.8cm.
horizontal traffic signs, polyline, computer vision, laser scanning, Velodyne LiDAR, point cloud, convolutional neural networks, object detection, U-Net, semantic segmentation, deep learning, PCL, QGIS, Keras, TensorFlow
Hradiš Michal, Ing., Ph.D. (DCGM FIT BUT), člen
Janoušek Vladimír, doc. Ing., Ph.D. (DITS FIT BUT), člen
Peringer Petr, Dr. Ing. (DITS FIT BUT), člen
Rozman Jaroslav, Ing., Ph.D. (DITS FIT BUT), člen
Smrčka Aleš, Ing., Ph.D. (DITS FIT BUT), člen
@mastersthesis{FITMT23519,
author = "Peter Marko",
type = "Master's thesis",
title = "Detekce objekt\r{u} v laserov\'{y}ch skenech pomoc\'{i} konvolu\v{c}n\'{i}ch neuronov\'{y}ch s\'{i}t\'{i}",
school = "Brno University of Technology, Faculty of Information Technology",
year = 2021,
location = "Brno, CZ",
language = "czech",
url = "https://www.fit.vut.cz/study/thesis/23519/"
}