首页|RNGDet++: Road Network Graph Detection by Transformer With Instance Segmentation and Multi-Scale Features Enhancement

RNGDet++: Road Network Graph Detection by Transformer With Instance Segmentation and Multi-Scale Features Enhancement

扫码查看
原文链接
  • NETL
  • NSTL
  • IEEE
The road network graph is a critical component for downstream tasks in autonomous driving, such as global route planning and navigation. In the past years, road network graphs are usually annotated by human experts manually, which is time-consuming and labor-intensive. To annotate road network graphs effectively and efficiently, automatic algorithms for road network graph detection are demanded. Most existing methods either adopt a post-processing step on semantic segmentation maps to produce road network graphs, or propose graph-based algorithms to directly predict the graphs. However, these works suffer from hard-coded algorithms and inferior performance. To enhance the previous state-of-the-art (SOTA) method RNGDet, we add an instance segmentation head to better supervise the training, and enable the network to leverage multi-scale features of the backbone. Since the new proposed approach is improved from RNGDet, we name it RNGDet++. Experimental results show that our RNGDet++ outperforms baseline methods in terms of almost all evaluation metrics on two large-scale public datasets.

RoadsFeature extractionTask analysisTransformersImage edge detectionAutonomous vehiclesPrediction algorithms

Zhenhua Xu、Yuxuan Liu、Yuxiang Sun、Ming Liu、Lujia Wang

展开 >

Hong Kong University of Science and Technology, Guangzhou, Guangdong, China

Department of Mechanical Engineering, Hong Kong Polytechnic University, Kowloon, Hong Kong

Hong Kong University of Science and Technology, Hong Kong, SAR, China|HKUST Shenzhen, Hong Kong Collaborative Innovation Research Institute, Shenzhen, China

Hong Kong University of Science and Technology, Hong Kong, SAR, China|Clear Water Bay Institute of Autonomous Driving (Shenzhen), Shenzhen, China

展开 >

2023

10.1109/LRA.2023.3264723

IEEE robotics and automation letters

IEEE robotics and automation letters

ISSN：
年,卷(期)：2023.8(5)
  • 1
  • 39