A Novel Real-Time Autonomous Crack Inspection System Based on Unmanned Aerial Vehicles

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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Author(s)

  • Kwai-Wa Tse
  • Rendong Pi
  • Yuxiang Sun
  • Chih-Yung Wen
  • Yurong Feng

Detail(s)

Original languageEnglish
Article number3418
Journal / PublicationSensors
Volume23
Issue number7
Online published24 Mar 2023
Publication statusPublished - Apr 2023
Externally publishedYes

Link(s)

Abstract

Traditional methods on crack inspection for large infrastructures require a number of structural health inspection devices and instruments. They usually use the signal changes caused by physical deformations from cracks to detect the cracks, which is time-consuming and cost-ineffective. In this work, we propose a novel real-time crack inspection system based on unmanned aerial vehicles for real-world applications. The proposed system successfully detects and classifies various types of cracks. It can accurately find the crack positions in the world coordinate system. Our detector is based on an improved YOLOv4 with an attention module, which produces 90.02% mean average precision (mAP) and outperforms the YOLOv4-original by 5.23% in terms of mAP. The proposed system is low-cost and lightweight. Moreover, it is not restricted by navigation trajectories. The experimental results demonstrate the robustness and effectiveness of our system in real-world crack inspection tasks. © 2023 by the authors.

Research Area(s)

  • attention module, autonomous inspection, crack detection, crack localization, deep learning, UAS, unmanned aerial vehicles, YOLOv4

Citation Format(s)

A Novel Real-Time Autonomous Crack Inspection System Based on Unmanned Aerial Vehicles. / Tse, Kwai-Wa; Pi, Rendong; Sun, Yuxiang et al.
In: Sensors, Vol. 23, No. 7, 3418, 04.2023.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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