Projekter pr. år
Abstract
The Sewer-ML dataset consists of 1.3 million images annotated by professional sewer inspectors from three different utility companies across nine years. Together with the dataset, we also present a benchmark algorithm and a novel metric for assessing performance. The benchmark algorithm is a result of evaluating 12 state-of-the-art algorithms, six from the sewer defect classification domain and six from the multi-label classification domain, and combining the best performing algorithms. The novel metric is a class-importance weighted F2 score, F2-CIW, reflecting the economic impact of each class, used together with the normal pipe F1 score, F1-Normal. The benchmark algorithm achieves an F2-CIW score of 55.11% and F1-Normal score of 90.94%, leaving ample room for improvement on the Sewer-ML dataset. The code, models, and dataset are available at the project page http://vap.aau.dk/sewer-ml
Originalsprog | Engelsk |
---|---|
Titel | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Antal sider | 12 |
Udgivelsessted | 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) |
Forlag | IEEE |
Publikationsdato | 2021 |
Sider | 13451-13462 |
Artikelnummer | 9577322 |
ISBN (Trykt) | 978-1-6654-4510-8 |
ISBN (Elektronisk) | 978-1-6654-4509-2 |
DOI | |
Status | Udgivet - 2021 |
Begivenhed | 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Virtual, Nashville, USA Varighed: 19 jun. 2021 → 25 jun. 2021 http://cvpr2021.thecvf.com/ |
Konference
Konference | 2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
---|---|
Lokation | Virtual |
Land/Område | USA |
By | Nashville |
Periode | 19/06/2021 → 25/06/2021 |
Internetadresse |
Navn | I E E E Conference on Computer Vision and Pattern Recognition. Proceedings |
---|---|
ISSN | 1063-6919 |
Fingeraftryk
Dyk ned i forskningsemnerne om 'Sewer-ML: A Multi-Label Sewer Defect Classification Dataset and Benchmark'. Sammen danner de et unikt fingeraftryk.Projekter
- 1 Afsluttet
-
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
Impacts
-
Robotter opsporer defekter i kloaksystemer
Thomas B. Moeslund (Deltager), Chris Holmberg Bahnsen (Deltager) & Joakim Bruslund Haurum (Deltager)
Impact: Økonomisk impact, Anden impact
Presse/Medier
-
FORSKER I SPILDEVAND: Kontinuerlig inspektion af kloakrør kan spare forsyninger for et trecifret millionbeløb – om året
15/05/2022
1 element af Mediedækning
Presse/medie
-
-
Data og algoritmer skal hjælpe med at vedligeholde vores kloakker
Joakim Bruslund Haurum & Thomas B. Moeslund
21/06/2021
1 Mediebidrag
Presse/medie
-
Multi-scale hybrid vision transformer and Sinkhorn tokenizer for sewer defect classification
Haurum, J. B., Madadi, M., Guerrero, S. E. & Moeslund, T. B., dec. 2022, I: Automation in Construction. 144, 104614.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
Åben adgangFil4 Citationer (Scopus)234 Downloads (Pure) -
Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder
Haurum, J. B., Madadi, M., Guerrero, S. E. & Moeslund, T. B., 2022, Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022. IEEE, s. 1441-1452 12 s. (IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)).Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review
Åben adgang4 Citationer (Scopus)
Forskningsdatasæt
-
Sewer-ML
Haurum, J. B. (Ophavsperson) & Moeslund, T. B. (Ophavsperson), sciencedata.dk, 18 jun. 2021
https://forms.gle/hBaPtoweZumZAi4u9 og et link mere, https://vap.aau.dk/sewer-ml/ (vis færre)
Datasæt