A Deep Learning-Based Approach for Road Pothole Detection in Timor Leste

Vosco Pereira, Satoshi Tamura, Satoru Hayamizu, Hidekazu Fukai

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

14 Citations (Scopus)

Abstract

This research proposes a low-cost solution for detecting road potholes image by using convolutional neural network (CNN). Our model is trained entirely on the image which collected from several different places and has variation such as in wet, dry and shady conditions. The experiment using the 500 testing images showed that our model can achieve (99.80 %) of Accuracy, Precision (100%), Recall (99.60%), and F-Measure (99.60%) simultaneously.

Original languageEnglish
Title of host publicationProceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages279-284
Number of pages6
ISBN (Electronic)9781538645222
DOIs
Publication statusPublished - 2018 Sep 28
Externally publishedYes
Event2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018 - Singapore, Singapore
Duration: 2018 Jul 312018 Aug 2

Publication series

NameProceedings of the 2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018

Conference

Conference2018 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2018
Country/TerritorySingapore
CitySingapore
Period18/7/3118/8/2

Keywords

  • Convolutional Neural Network
  • Deep Learning
  • Image Classification
  • Potholes

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems and Management
  • Management Science and Operations Research
  • Instrumentation
  • Computer Networks and Communications
  • Computer Science Applications

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