Broken Road Detection Methods Comparison: A Literature Survey

Authors

  • Indra Yustiana Nusa Putra University
  • Somantri Nusa Putra University
  • Dudih Gustian Nusa Putra University
  • Anggy Pradifta Junfithrana Nusa Putra University
  • Satish Kumar Damodar University of Twente

DOI:

https://doi.org/10.52005/ijeat.v5i2.75

Keywords:

Broken Road, Image Detection, Literature, Machine Learning

Abstract

Roads are infrastructure built to facilitate regional development. Good road conditions will certainly provide a sense of comfort for every vehicle that will pass through it. For that, care and attention to road conditions needs to be done. The occurrence of damage to the road will hinder the development process. Currently, detection of damaged roads is still done manually using human resource. It makes the detection process take quite a lot of time to determine how bad the damage is. So there needs a way to help improve time efficiency and accuracy in detecting damaged roads. One of them is by utilizing machine learning technology. In this paper, we will discuss what methodology can be use and their comparisons to be able to use appropriate and effective methodologies to detect cases of damaged roads

Author Biographies

Indra Yustiana , Nusa Putra University

Departmen Information Technology

Somantri, Nusa Putra University

Department of Informatics Engineering

Dudih Gustian, Nusa Putra University

Department of Information System

Anggy Pradifta Junfithrana, Nusa Putra University

Department of Electrical Engineering

Satish Kumar Damodar, University of Twente

Postdoctoral Reseracher – European Scientific Institute

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Published

2022-11-23

How to Cite

Indra Yustiana, Somantri, Dudih Gustian, Anggy Pradifta Junfithrana, & Satish Kumar Damodar. (2022). Broken Road Detection Methods Comparison: A Literature Survey. INTERNATIONAL JOURNAL ENGINEERING AND APPLIED TECHNOLOGY (IJEAT), 5(2), 16–23. https://doi.org/10.52005/ijeat.v5i2.75