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Publication detail
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
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" }