Semantic Segmentation of Paved Road and Pothole Image Using U-Net Architecture

Vosco Pereira, Satoshi Tamura, Satoru Hayamizu, Hidekazu Fukai

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

1 Citation (Scopus)

Abstract

Research on road monitoring system has been actively conducted by using both machine learning and deep learning technique. One of our nearest goal in the framework of road condition monitoring system is to segment all road related object and provide a technical report regarding road condition. Our final objective is to develop a community participant-based system for road condition monitoring. As one of our task, in this research, we start with the segmentation of road and pothole. To conduct this task we proposed a semantic segmentation method for road and pothole image segmentation by using one of the famous deep learning technique U-Net. Various condition of road images were used for training and validating the model. The experiment result showed that U-Net model can achieve 97 % of accuracy and 0.86 of mean Intersection Over Union (mIOU).

Original languageEnglish
Title of host publicationProceedings - 2019 International Conference on Advanced Informatics
Subtitle of host publicationConcepts, Theory, and Applications, ICAICTA 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728134505
DOIs
Publication statusPublished - 2019 Sep
Externally publishedYes
Event2019 International Conference on Advanced Informatics: Concepts, Theory, and Applications, ICAICTA 2019 - Yogyakarta, Indonesia
Duration: 2019 Sep 202019 Sep 22

Publication series

NameProceedings - 2019 International Conference on Advanced Informatics: Concepts, Theory, and Applications, ICAICTA 2019

Conference

Conference2019 International Conference on Advanced Informatics: Concepts, Theory, and Applications, ICAICTA 2019
CountryIndonesia
CityYogyakarta
Period19/9/2019/9/22

Keywords

  • Deep Learning
  • Pothole
  • Road
  • Road Condition Monitoring
  • Semantic Segmentation
  • U-Net

ASJC Scopus subject areas

  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems

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