Detection of limit situation in segmentation network via CNN

Junho Song, Sangkyoo Park, Myotaeg Lim

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The ability to detect limit situation is an essential element for ensuring safety in semantic segmentation task in self-driving system. In this paper, we study the detection of limit situations on the results of the image semantic segmentation network, and propose a framework consisting of convolution layers and fully connected layers. The mIoU value is deduced to evaluate a performance of semantic segmentation on the image obtained from the front vertical camera of the actual vehicle. The proposed network shows 90.51% accuracy in Hyundai Motor Group road image dataset for reasoning as a result of verification of the test set.

Original languageEnglish
Title of host publication2020 20th International Conference on Control, Automation and Systems, ICCAS 2020
PublisherIEEE Computer Society
Pages892-894
Number of pages3
ISBN (Electronic)9788993215205
DOIs
Publication statusPublished - 2020 Oct 13
Event20th International Conference on Control, Automation and Systems, ICCAS 2020 - Busan, Korea, Republic of
Duration: 2020 Oct 132020 Oct 16

Publication series

NameInternational Conference on Control, Automation and Systems
Volume2020-October
ISSN (Print)1598-7833

Conference

Conference20th International Conference on Control, Automation and Systems, ICCAS 2020
CountryKorea, Republic of
CityBusan
Period20/10/1320/10/16

Keywords

  • CNN
  • Detection of limit situation
  • Self-driving system
  • Semantic segmentation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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