Road Detection in Semi-Structured Outdoor Environments
The SUNSET benchmark and TEAR method for end-to-end road and boundary detection
Research Project Paper
Research Problem
This project studies end-to-end detection of roads and their boundary lines in semi-structured outdoor environments that lack artificial lane markings but retain visible road trajectories.
Contributions
- Benchmark construction. Selected semi-structured scenes from the public DeepScene, YCOR, and GOOSE datasets. Using an enhanced Labelme curve annotation tool, we labeled left and right road boundaries with high-order Bézier curves and paired them with road instances to build the SUNSET dataset and benchmark.
- Method design. Contributed to TEAR, which uses interchangeable dual instance decoders to disentangle road and boundary-line instances and hierarchical bipartite matching to associate them.
- Research output. TEAR of the SUNSET introduces the benchmark and end-to-end method and was accepted by IEEE ICME 2026 (CCF B).