Projekter pr. år
Abstract
Sewer pipes are currently manually inspected by trained inspectors, making the process prone to human errors, which can be potentially critical. There is therefore a great research and industry interest in automating the sewer inspection process. Previous research have been focused on working with 2D image data, similar to how inspections are currently conducted. There is, however, a clear potential for utilizing recent advances within 3D computer vision for this task. In this paper we investigate the feasibility of applying two modern deep learning methods, DGCNN and PointNet, on a new publicly available sewer point cloud dataset. As point cloud data from real sewers is scarce, we investigate using synthetic data to bootstrap the training process. We investigate four data scenarios, and find that training on synthetic data and fine-tune on real data gives the best results, increasing the metrics by 6-10 percentage points for the best model. Data and code is available at https://bitbucket.org/aauvap/sewer3dclassification.
Originalsprog | Engelsk |
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Titel | Proceedings of the 16th International Conference on Computer Vision Theory and Applications (VISAPP) |
Redaktører | Giovanni Maria Farinella, Petia Radeva, Jose Braz, Kadi Bouatouch |
Antal sider | 10 |
Vol/bind | 5 |
Forlag | SCITEPRESS Digital Library |
Publikationsdato | 2021 |
Sider | 891-900 |
ISBN (Elektronisk) | 9789897584886 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | International Conference on Computer Vision Theory and Applications - Varighed: 8 feb. 2021 → 10 feb. 2021 Konferencens nummer: 16 http://www.visapp.visigrapp.org/ |
Konference
Konference | International Conference on Computer Vision Theory and Applications |
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Nummer | 16 |
Periode | 08/02/2021 → 10/02/2021 |
Internetadresse |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Sewer Defect Classification using Synthetic Point Clouds'. Sammen danner de et unikt fingeraftryk.Projekter
- 1 Afsluttet
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ASIR: Automated Sewer Inspection Robot
Moeslund, T. B., Haurum, J. B., Bahnsen, C. H. & Hansen, B. D.
01/11/2018 → 30/04/2022
Projekter: Projekt › Forskning
Forskningsdatasæt
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AAU Sewer Defect Point Cloud Dataset
Haurum, J. B. (Ophavsperson), Allahham, M. M. J. (Ophavsperson), Lynge, M. S. (Ophavsperson), Henriksen, K. S. (Ophavsperson), Nikolov, I. A. (Ophavsperson) & Moeslund, T. B. (Ophavsperson), Kaggle, 2021
https://www.kaggle.com/aalborguniversity/sewerpointclouds
Datasæt