Perhaps surprisingly sewerage infrastructure is one of the most costly infrastructures in modern society. Sewer pipes are manually inspected to determine whether the pipes are defective. However, this process is limited by the number of qualified inspectors and the time it takes to inspect a pipe. Automatization of this process is therefore of high interest. So far, the success of computer vision approaches for sewer defect classification has been limited when compared to the success in other fields mainly due to the lack of public datasets. To this end, in this work we present a large novel and publicly available multi-label classification dataset for image-based sewer defect classification called Sewer-ML.
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
Original languageEnglish
Title of host publication2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Number of pages12
Place of Publication2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Publication date2021
Article number9577322
ISBN (Print)978-1-6654-4510-8
ISBN (Electronic)978-1-6654-4509-2
Publication statusPublished - 2021
Event2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) - Virtual, Nashville, United States
Duration: 19 Jun 202125 Jun 2021


Conference2021 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Country/TerritoryUnited States
Internet address
SeriesI E E E Conference on Computer Vision and Pattern Recognition. Proceedings


  • Computer Vision
  • Sewer Inspection
  • Sewer Defect
  • Multi-Label Classification
  • Dataset
  • Defect Classification


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