Publication detail

Detection of road surface defects from data acquired by a laser scanner

MYŠKA, V.

Original Title

Detection of road surface defects from data acquired by a laser scanner

Type

conference paper

Language

English

Original Abstract

Research in the field of automatic detection of road surface defects has been relatively widespread in recent years. Most of the existing works solve this issue by processing the image acquired by camera technology. The contribution of this study is the proposal of the LRS-CNN algorithm for the detection of defects on road surfaces based on their laser scans. The advantage of LRS-CNN is the ability to detect so-called microcracks, which can not be recognized from camera recordings. We have also found that transfer learning methods are not suitable for the use of road defect detection from their laser scans. Our LRS-CNN algorithm has been trained on unique nonpublic data and is able to achieve up to 99.33 % of success depending on the type of task.

Keywords

road damage detection, road surface laser scan, deep learning

Authors

MYŠKA, V.

Released

27. 4. 2021

Publisher

Brno Univeristy of Technology, Faculty of Electrical Engineering and Communication

Location

Brno

ISBN

978-80-214-5943-4

Book

Proceedings II of the 27th Conference STUDENT EEICT 2021 selected papers

Edition

1

Pages from

275

Pages to

279

Pages count

5

URL

https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_2.pdf

BibTex

@inproceedings{BUT171457,
  author="Vojtěch {Myška}",
  title="Detection of road surface defects from data acquired by a laser scanner",
  booktitle="Proceedings II of the 27th Conference STUDENT EEICT 2021 selected papers",
  year="2021",
  series="1",
  pages="275--279",
  publisher="Brno Univeristy of Technology, Faculty of Electrical Engineering and Communication",
  address="Brno",
  doi="10.13164/eeict.2021.275",
  isbn="978-80-214-5943-4",
  url="https://www.fekt.vut.cz/conf/EEICT/archiv/sborniky/EEICT_2021_sbornik_2.pdf"
}