Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105374
Title: | A novel real-time autonomous crack inspection system based on unmanned aerial vehicles | Authors: | Tse, KW Pi, R Sun, Y Wen, CY Feng, Y |
Issue Date: | Apr-2023 | Source: | Sensors, Apr. 2023, v. 23, no. 7, 3418 | 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. | Keywords: | Attention module Autonomous inspection Crack detection Crack localization Deep learning UAS Unmanned aerial vehicles YOLOv4 |
Publisher: | Molecular Diversity Preservation International (MDPI) | Journal: | Sensors | EISSN: | 1424-8220 | DOI: | 10.3390/s23073418 | Rights: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The following publication Tse K-W, Pi R, Sun Y, Wen C-Y, Feng Y. A Novel Real-Time Autonomous Crack Inspection System Based on Unmanned Aerial Vehicles. Sensors. 2023; 23(7):3418 is available at https://doi.org/10.3390/s23073418. |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
sensors-23-03418.pdf | 9.99 MB | Adobe PDF | View/Open |
Page views
12
Citations as of Jul 7, 2024
Downloads
2
Citations as of Jul 7, 2024
SCOPUSTM
Citations
5
Citations as of Jul 4, 2024
WEB OF SCIENCETM
Citations
4
Citations as of Jul 4, 2024
![](/image/google_scholar.jpg)
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.