Convolution neural networks for pothole detection of critical road infrastructure

Anup Kumar Pandey, Vasile Palade, Rahat Iqbal, Tomasz Maniak, Charalampos Karyotis, Stephen Akuma

Research output: Contribution to journalArticlepeer-review

41 Citations (Scopus)
202 Downloads (Pure)

Abstract

A well developed and maintained highway infrastructure is essential for the economic and social prosperity of modern societies. Highway maintenance poses significant challenges pertaining to the ever-increasing ongoing traffic, insufficient budget allocations and lack of resources. Road potholes detection and timely repair is a major contributing factor to sustaining a safe and resilient critical road infrastructure. Current pothole detection methods require laborious manual inspection of roads and lack in terms of accuracy and inference speed. This paper proposes a novel application of Convolutional Neural Networks on accelerometer data for pothole detection. Data is collected using an iOS smartphone installed on the dashboard of a car, running a dedicated application. The experimental results show that the proposed CNN approach has a significant advantage over the existing solutions, with respect to accuracy and computational complexity in pothole detection.

Original languageEnglish
Article number107725
JournalComputers and Electrical Engineering
Volume99
Early online date16 Feb 2022
DOIs
Publication statusPublished - Apr 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

Keywords

  • Accelerometer data
  • Convolution neural networks
  • Crowdsource data
  • Highway maintenance
  • Pothole detection

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)
  • Electrical and Electronic Engineering

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