Detection Technology of Traffic Marking Edge

Tingze Zhang, Huan Gao

Research output: Chapter in Book/Conference paperConference paperpeer-review

Abstract

In recent years, the Intelligent Transportation System (ITS) has attracted growing attention from experts and scholars. Edge-based traffic sign image segmentation is a key technology in the field of traffic sign recognition. Therefore, traffic sign edge detection technology is of vital importance. Edge detection is an image processing technology, which can quickly and accurately extract image edge information. Thus, improving the accuracy of edge detection has become a significant research direction. Furthermore, due to the influence of the complexity of external factors on actual images, the conventional edge detection algorithms present insufficient adaptability, so the improved performance of the operators needs to be further investigated. This paper mainly studies several traditional edge detection techniques and applies them to traffic sign maps using MATLAB. After comparing the results, we proposed an improved Canny algorithm and analyzes its applicability in traffic sign edge detection under different external interference factors.

Original languageEnglish
Title of host publicationProceedings of 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2020
EditorsHuabo Sun
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages608-611
Number of pages4
ISBN (Electronic)9781728199481
DOIs
Publication statusPublished - 14 Oct 2020
Event2nd IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2020 - Weihai, China
Duration: 14 Oct 202016 Oct 2020

Publication series

NameProceedings of 2020 IEEE 2nd International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2020

Conference

Conference2nd IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2020
Country/TerritoryChina
CityWeihai
Period14/10/2016/10/20

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