  Mainstream lane marker detection methods are implemented by predicting the
overall structure and deriving parametric curves through post-processing.
Complex lane line shapes require high-dimensional output of CNNs to model
global structures, which further increases the demand for model capacity and
training data. In contrast, the locality of a lane marker has finite geometric
variations and spatial coverage. We propose a novel lane marker detection
solution, FOLOLane, that focuses on modeling local patterns and achieving
prediction of global structures in a bottom-up manner. Specifically, the CNN
models lowcomplexity local patterns with two separate heads, the first one
predicts the existence of key points, and the second refines the location of
key points in the local range and correlates key points of the same lane line.
The locality of the task is consistent with the limited FOV of the feature in
CNN, which in turn leads to more stable training and better generalization. In
addition, an efficiency-oriented decoding algorithm was proposed as well as a
greedy one, which achieving 36% runtime gains at the cost of negligible
performance degradation. Both of the two decoders integrated local information
into the global geometry of lane markers. In the absence of a complex network
architecture design, the proposed method greatly outperforms all existing
methods on public datasets while achieving the best state-of-the-art results
and real-time processing simultaneously.
