Deep learning; LiDAR; Machine learning; Mobile mapping systems; Point clouds; Semantic segmentation; Laser scanning data; Learning methods; Machine-learning; Manual labeling; Point-clouds; Segmentation algorithms; Engineering (all); Computer Science Applications; Artificial Intelligence; General Engineering
Résumé :
[en] Labelled point clouds are crucial to train supervised Deep Learning (DL) methods used for semantic segmentation. The objective of this research is to quantify discordances between the labels made by different people in order to assess whether such discordances can influence the success rates of a DL based semantic segmentation algorithm. An urban point cloud of 30 m road length in Santiago de Compostela (Spain) was labelled two times by ten persons. Discordances and its significance in manual labelling between individuals and rounds were calculated. In addition, a ratio test to signify discordance and concordance was proposed. Results show that most of the points were labelled accordingly with the same class by all the people. However, there were many points that were labelled with two or more classes. Class curb presented 5.9% of discordant points and 3.2 discordances for each point with concordance by all people. In addition, the percentage of significative labelling differences of the class curb was 86.7% comparing all the people in the same round and 100% comparing rounds of each person. Analysing the semantic segmentation results with a DL based algorithm, PointNet++, the percentage of concordance points are related with F-score value in R2 = 0.765, posing that manual labelling has significant impact on results of DL-based semantic segmentation methods.
Disciplines :
Ingénierie civile
Auteur, co-auteur :
González-Collazo, Silvia María ; CINTECX, Universidade de Vigo, Vigo, Spain
Balado, Jesús ; CINTECX, Universidade de Vigo, Vigo, Spain
González, Elena; CINTECX, Universidade de Vigo, Vigo, Spain
NURUNNABI, Abdul Awal Md ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
A discordance analysis in manual labelling of urban mobile laser scanning data used for deep learning based semantic segmentation
The authors would like to thank the point cloud labelling volunteers for their time and effort. This research has received funding from Xunta de Galicia through human resources grant (ED481B-2019-061) and from the Government of Spain through project PID2019-105221RB-C43 funded by MCIN/AEI/10.13039/501100011033. Mr Nurunnabi is with the Project 2019-05-030-24, SOLSTICE - Programme Fonds Européen de Developpment Régional (FEDER)/Ministère de l'Economie of the G. D. of Luxembourg.This paper was carried out in the framework of the InfraROB project (Maintaining integrity, performance and safety of the road infrastructure through autonomous robotized solutions and modularization), which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 955337. It reflects only the authors’ views. Neither the European Climate, Infrastructure, and Environment Executive Agency (CINEA) nor the European Commission is in any way responsible for any use that may be made of the information it contains. Authors also would like to thank to CESGA for the use of their servers. Funding for open access charge: Universidade de Vigo/CISUG.The authors would like to thank the point cloud labelling volunteers for their time and effort. This research has received funding from Xunta de Galicia through human resources grant (ED481B-2019-061) and from the Government of Spain through project PID2019-105221RB-C43 funded by MCIN/AEI/10.13039/501100011033. Mr Nurunnabi is with the Project 2019-05-030-24, SOLSTICE - Programme Fonds Européen de Developpment Régional (FEDER)/Ministère de l’Economie of the G. D. of Luxembourg.This paper was carried out in the framework of the InfraROB project (Maintaining integrity, performance and safety of the road infrastructure through autonomous robotized solutions and modularization), which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 955337. It reflects only the authors’ views. Neither the European Climate, Infrastructure, and Environment Executive Agency (CINEA) nor the European Commission is in any way responsible for any use that may be made of the information it contains. Authors also would like to thank to CESGA for the use of their servers. Funding for open access charge: Universidade de Vigo/CISUG.
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