An End-to-End Multi-Task Learning Model for Drivable Road Detection via Edge Refinement and Geometric Deformation-Reference-Cited by-同舟云学术

An End-to-End Multi-Task Learning Model for Drivable Road Detection via Edge Refinement and Geometric Deformation

Author:

Li Keqiang^ORCID,Xiong Hui^ORCID,Yu Dameng,Liu Jinxin^ORCID,Guo Yu'ang,Wang Jianqiang^ORCID

Funder

National Natural Science Foundation of China

Tsinghua University-Didi Joint Research Center for Future Mobility

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

Cited by 6 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Multi-class Pixel Level Segmentation for Drivable Road Detection;Proceedings of the 6th International Conference on Communications and Cyber Physical Engineering;2024

3. FastRoadSeg: Fast Monocular Road Segmentation Network;IEEE Transactions on Intelligent Transportation Systems;2022-11

4. Terrain Traversability Mapping Based on LiDAR and Camera Fusion;2022 8th International Conference on Automation, Robotics and Applications (ICARA);2022-02-18