Semi-Supervised Lane Detection With Deep Hough Transform

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Abstract

Current work on lane detection relies on large manually annotated datasets. We reduce the dependency on annotations by leveraging massive cheaply available unlabelled data. We propose a novel loss function exploiting geometric knowledge of lanes in Hough space, where a lane can be identified as a local maximum. By splitting lanes into separate channels, we can localize each lane via simple global max-pooling. The location of the maximum encodes the layout of a lane, while the intensity indicates the the probability of a lane being present. Maximizing the log-probability of the maximal bins helps neural networks find lanes without labels. On the CULane and TuSimple datasets, we show that the proposed Hough Transform loss improves performance significantly by learning from large amounts of unlabelled images.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing (ICIP)
Subtitle of host publicationProceedings
Place of PublicationPiscataway
PublisherIEEE
Pages1514-1518
Number of pages5
ISBN (Electronic)978-1-6654-4115-5
ISBN (Print)978-1-6654-3102-6
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Conference on Image Processing (ICIP) - Virtual at Anchorage, United States
Duration: 19 Sep 202122 Sep 2021

Conference

Conference2021 IEEE International Conference on Image Processing (ICIP)
CountryUnited States
CityVirtual at Anchorage
Period19/09/2122/09/21

Bibliographical note

Accepted author manuscript

Keywords

  • Lane detection
  • Hough Transform
  • semi-supervised learning

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