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.