Original title: Detection Of Road Surface Defects From Data Acquired By A Laser Scanner
Authors: Myska, Vojtech
Document type: Papers
Language: eng
Publisher: Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
Abstract: Research in the field of automatic detection of road surface defects has been relativelywidespread in recent years. Most of the existing works solve this issue by processing the imageacquired by camera technology. The contribution of this study is the proposal of the LRS-CNN algorithmfor the detection of defects on road surfaces based on their laser scans. The advantage ofLRS-CNN is the ability to detect so-called microcracks, which can not be recognized from camerarecordings. We have also found that transfer learning methods are not suitable for the use of road defectdetection from their laser scans. Our LRS-CNN algorithm has been trained on unique nonpublicdata and is able to achieve up to 99.33% of success depending on the type of task.
Keywords: deep learning; road damage detection; road surface laser scan
Host item entry: Proceedings II of the 27st Conference STUDENT EEICT 2021: Selected papers, ISBN 978-80-214-5943-4

Institution: Brno University of Technology (web)
Document availability information: Fulltext is available in the Brno University of Technology Digital Library.
Original record: http://hdl.handle.net/11012/200857

Permalink: http://www.nusl.cz/ntk/nusl-447901


The record appears in these collections:
Universities and colleges > Public universities > Brno University of Technology
Conference materials > Papers