A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Pavement distress detection using terrestrial laser scanning point clouds – Accuracy evaluation and algorithm comparison
Tekijät: Feng Ziyi, El Issaoui Aimad, Lehtomäki Matti, Ingman Matias, Kaartinen Harri, Kukko Antero, Savela Joona, Hyyppä Hannu, Hyyppä Juha
Kustantaja: Elsevier
Julkaisuvuosi: 2022
Lehti: ISPRS Open Journal of Photogrammetry and Remote Sensing
Tietokannassa oleva lehden nimi: ISPRS Open Journal of Photogrammetry and Remote Sensing
Artikkelin numero: 100010
Vuosikerta: 3
ISSN: 2667-3932
eISSN: 2667-3932
DOI: https://doi.org/10.1016/j.ophoto.2021.100010
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Kokonaan avoin julkaisukanava
Verkko-osoite: https://doi.org/10.1016/j.ophoto.2021.100010
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/381253962
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
In this paper, we compared five crack detection algorithms using terrestrial laser scanner (TLS) point clouds. The methods are developed based on common point cloud processing knowledge in along- and across-track profiles, surface fitting or local pointwise features, with or without machine learning. The crack area and volume were calculated from the crack points detected by the algorithms. The completeness, correctness, and F1 score of each algorithm were computed against manually collected references. Ten 1-m-by-3.5-m plots containing 75 distresses of six distress types (depression, disintegration, pothole, longitudinal, transverse, and alligator cracks) were selected to explain variability of distresses from a 3-km-long-road. For crack detection at plot level, the best algorithm achieved a completeness of up to 0.844, a correctness of up to 0.853, and an F1 score of up to 0.849. The best algorithm’s overall (ten plots combined) completeness, correctness, and F1 score were 0.642, 0.735, and 0.685 respectively. For the crack area estimation, the overall mean absolute percentage errors (MAPE) of the two best algorithms were 19.8% and 20.3%. In the crack volume estimation, the two best algorithms resulted in 19.3% and 14.5% MAPE. When the plots were grouped based on crack detection complexity, in the ‘easy’ category, the best algorithm reached a crack area estimation MAPE of 8.9%, while for crack volume estimation, the MAPE obtained from the best algorithm was 0.7%.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
