TY - JOUR
T1 - Topo-Boundary: A Benchmark Dataset on Topological Road-Boundary Detection Using Aerial Images for Autonomous Driving
AU - Xu, Zhenhua
AU - Sun, Yuxiang
AU - Liu, Ming
N1 - Funding Information:
Manuscript received February 24, 2021; accepted June 20, 2021. Date of publication July 16, 2021; date of current version July 29, 2021. This letter was recommended for publication by Associate Editor B. C. Arrue and Editor P. Pounds upon evaluation of the reviewers’ comments. This work was supported in part by Collaborative Research Fund by Research Grants Council Hong Kong under Grant C4063-18G, in part by the Department of Science and Technology of Guangdong Province Fund under Grant GDST20EG54, and in part by Zhongshan Municipal Science and Technology Bureau Fund under Grant ZSST21EG06, awarded to Prof. Ming Liu. (Corresponding author: Ming Liu.) Zhenhua Xu is with the Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong (e-mail: [email protected]).
Publisher Copyright:
© 2016 IEEE.
PY - 2021/10
Y1 - 2021/10
N2 - Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online road-boundary detection using on-vehicle cameras/Lidars, offline detection using aerial images could alleviate the severe occlusion issue. Moreover, the offline detection results can be directly employed to annotate high-definition (HD) maps. In recent years, deep-learning technologies have been used in offline detection. But there still lacks a publicly available dataset for this task, which hinders the research progress in this area. So in this letter, we propose a new benchmark dataset, named Topo-boundary, for offline topological road-boundary detection. The dataset contains 25,295 1000×1000-sized 4-channel aerial images. Each image is provided with 8 training labels for different sub-tasks. We also design a new entropy-based metric for connectivity evaluation, which could better handle noises or outliers. We implement and evaluate 3 segmentation-based baselines and 5 graph-based baselines using the dataset. We also propose a new imitation-learning-based baseline which is enhanced from our previous work. The superiority of our enhancement is demonstrated from the comparison. The dataset and our-implemented code for the baselines are available at https://tonyxuqaq.github.io/Topo-boundary/.
AB - Road-boundary detection is important for autonomous driving. It can be used to constrain autonomous vehicles running on road areas to ensure driving safety. Compared with online road-boundary detection using on-vehicle cameras/Lidars, offline detection using aerial images could alleviate the severe occlusion issue. Moreover, the offline detection results can be directly employed to annotate high-definition (HD) maps. In recent years, deep-learning technologies have been used in offline detection. But there still lacks a publicly available dataset for this task, which hinders the research progress in this area. So in this letter, we propose a new benchmark dataset, named Topo-boundary, for offline topological road-boundary detection. The dataset contains 25,295 1000×1000-sized 4-channel aerial images. Each image is provided with 8 training labels for different sub-tasks. We also design a new entropy-based metric for connectivity evaluation, which could better handle noises or outliers. We implement and evaluate 3 segmentation-based baselines and 5 graph-based baselines using the dataset. We also propose a new imitation-learning-based baseline which is enhanced from our previous work. The superiority of our enhancement is demonstrated from the comparison. The dataset and our-implemented code for the baselines are available at https://tonyxuqaq.github.io/Topo-boundary/.
KW - autonomous driving
KW - imitation learning
KW - large-scale dataset
KW - Road-boundary detection
UR - http://www.scopus.com/inward/record.url?scp=85110854972&partnerID=8YFLogxK
U2 - 10.1109/LRA.2021.3097512
DO - 10.1109/LRA.2021.3097512
M3 - Journal article
AN - SCOPUS:85110854972
SN - 2377-3766
VL - 6
SP - 7248
EP - 7255
JO - IEEE Robotics and Automation Letters
JF - IEEE Robotics and Automation Letters
IS - 4
M1 - 9488209
ER -