Lane Detection Using Edge Detection and Spatio-Temporal Incremental Clustering

Sayyidul Aulia Alamsyah, Djoko Purwanto, Muhammad Attamimi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

Nowadays, research on intelligent vehicle technology is widely brought out. One of the most important functions that must exist on the intelligent vehicle is lane detection. The purpose of the lane detection system is to alert the driver if the vehicle is moving too close to lane boundaries, such as a white line. However, not all roads have been equipped with white lines as the lane boundaries. In this study, a method to detect lane boundaries on roads that do not have white lines as the road boundaries are proposed. Canny edge detection and Hough transforms are used to extract the lane boundaries, then cluster the result using spatio-temporal incremental clustering. The output of this method is an image with lines that represent the lane boundaries. This method can detect 90% road boundary of 1193 straight road images correctly.

Original languageEnglish
Title of host publicationProceedings - 2021 International Seminar on Intelligent Technology and Its Application
Subtitle of host publicationIntelligent Systems for the New Normal Era, ISITIA 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages364-369
Number of pages6
ISBN (Electronic)9781665428477
DOIs
Publication statusPublished - 21 Jul 2021
Event2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021 - Virtual, Online
Duration: 21 Jul 202122 Jul 2021

Publication series

NameProceedings - 2021 International Seminar on Intelligent Technology and Its Application: Intelligent Systems for the New Normal Era, ISITIA 2021

Conference

Conference2021 International Seminar on Intelligent Technology and Its Application, ISITIA 2021
CityVirtual, Online
Period21/07/2122/07/21

Keywords

  • Intelligent Vehicle
  • Lane Detection
  • Spatio-Temporal Incremental Clustering

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