@inproceedings{6fec992962df4d309afa9869f172a9cf,
title = "Traffic Sign Detection and Recognition for Autonomous Driving in Virtual Simulation Environment",
abstract = "This study developed a traffic sign detection and recognition algorithm based on the RetinaNet. Two main aspects were revised to improve the detection of traffic signs: image cropping to address the issue of large image and small traffic signs and more anchors with various scales to detect traffic signs with different sizes and shapes. The proposed algorithm was trained and tested in a series of autonomous driving front-view images in a virtual simulation environment. Results show that the algorithm performed well under good illumination and weather conditions. The drawbacks are that it sometimes failed to detect objects under bad weather conditions like snow and failed to distinguish speed limit signs with different limit values.",
author = "Meixin Zhu and Yang, \{Hao Frank\} and Zhiyong Cui and Yinhai Wang",
note = "Publisher Copyright: {\textcopyright} ASCE. All rights reserved.; International Conference on Transportation and Development 2022, ICTD 2022 ; Conference date: 31-05-2022 Through 03-06-2022",
year = "2022",
doi = "10.1061/9780784484326.002",
language = "英语",
series = "International Conference on Transportation and Development 2022: Application of Emerging Technologies - Selected Papers from the Proceedings of the International Conference on Transportation and Development 2022",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "12--18",
editor = "Heng Wei",
booktitle = "International Conference on Transportation and Development 2022",
address = "美国",
}