Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/89329
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Title: Learning-based autonomous uav system for electrical and mechanical (E&M) device inspection
Authors: Feng, Y 
Tse, K 
Chen, S 
Wen, CY 
Li, B 
Issue Date: 2-Feb-2021
Source: Sensors (Switzerland), 2 Feb. 2021, v. 21, no. 4, 1385, p. 1-19
Abstract: The inspection of electrical and mechanical (E&M) devices using unmanned aerial vehicles (UAVs) has become an increasingly popular choice in the last decade due to their flexibility and mobility. UAVs have the potential to reduce human involvement in visual inspection tasks, which could increase efficiency and reduce risks. This paper presents a UAV system for autonomously performing E&M device inspection. The proposed system relies on learning-based detection for perception, multi-sensor fusion for localization, and path planning for fully autonomous inspection. The perception method utilizes semantic and spatial information generated by a 2-D object detector. The information is then fused with depth measurements for object state estimation. No prior knowledge about the location and category of the target device is needed. The system design is validated by flight experiments using a quadrotor platform. The result shows that the proposed UAV system enables the inspection mission autonomously and ensures a stable and collision-free flight.
Keywords: Autonomous inspection
Deep learning
Object detection
UAV
Publisher: Molecular Diversity Preservation International (MDPI)
Journal: Sensors (Switzerland) 
ISSN: 1424-8220
DOI: 10.3390/s21041385
Rights: © 2021 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 Feng, Y.; Tse, K.; Chen, S.; Wen, C.-Y.; Li, B. Learning-Based Autonomous UAV System for Electrical and Mechanical (E&M) Device Inspection. Sensors 2021, 21 (4), 1385, 1-19 is availiable at https://doi.org10.3390/s21041385
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