TopoMask: Instance-Mask-Based Formulation for the Road Topology Problem
  via Transformer-Based Architecture
M. Esat Kalfaoglu Halil Ibrahim Ozturk Ozsel Kilinc Alptekin Temizel
Driving scene understanding task involves detecting static elements such as
lanes, traffic signs, and traffic lights, and their relationships with each
other. To facilitate the development of comprehensive scene understanding
solutions using multiple camera views, a new dataset called Road Genome
(OpenLane-V2) has been released. This dataset allows for the exploration of
complex road connections and situations where lane markings may be absent.
Instead of using traditional lane markings, the lanes in this dataset are
represented by centerlines, which offer a more suitable representation of lanes
and their connections. In this study, we have introduced a new approach called
TopoMask for predicting centerlines in road topology. Unlike existing
approaches in the literature that rely on keypoints or parametric methods,
TopoMask utilizes an instance-mask based formulation with a transformer-based
architecture and, in order to enrich the mask instances with flow information,
a direction label representation is proposed. TopoMask have ranked 4th in the
OpenLane-V2 Score (OLS) and ranked 2nd in the F1 score of centerline prediction
in OpenLane Topology Challenge 2023. In comparison to the current
state-of-the-art method, TopoNet, the proposed method has achieved similar
performance in Frechet-based lane detection and outperformed TopoNet in
Chamfer-based lane detection without utilizing its scene graph neural network.