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

Vosco Pereira, Satoshi Tamura, Satoru Hayamizu, Hidekazu Fukai

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

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).

本文言語English
ホスト出版物のタイトルProceedings - 2019 International Conference on Advanced Informatics
ホスト出版物のサブタイトルConcepts, Theory, and Applications, ICAICTA 2019
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781728134505
DOI
出版ステータスPublished - 2019 9
外部発表はい
イベント2019 International Conference on Advanced Informatics: Concepts, Theory, and Applications, ICAICTA 2019 - Yogyakarta, Indonesia
継続期間: 2019 9 202019 9 22

出版物シリーズ

名前Proceedings - 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
国/地域Indonesia
CityYogyakarta
Period19/9/2019/9/22

ASJC Scopus subject areas

  • 情報システムおよび情報管理
  • 安全性、リスク、信頼性、品質管理
  • コンピュータ ネットワークおよび通信
  • ハードウェアとアーキテクチャ
  • 情報システム

フィンガープリント

「Semantic Segmentation of Paved Road and Pothole Image Using U-Net Architecture」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル