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International Journal of Aerospace Engineering
Volume 2019, Article ID 5137139, 14 pages
Research Article

Visual Inspection of the Aircraft Surface Using a Teleoperated Reconfigurable Climbing Robot and Enhanced Deep Learning Technique

1Singapore University of Technology and Design, Singapore 487372
2Department of Engineering and Technology, Universidad de Occidente, Campus Los Mochis, 81223, Mexico
3Department of Computer Science, Birla Institute of Technology and Science (BITS) Pilani, Pilani Campus, 333031, Vidyavihar, Rajasthan, India
4Department of Electrical Engineering, UET Lahore, NWL Campus 54890, Pakistan
5ST Engineering Aerospace, ST Engineering, Singapore 539938

Correspondence should be addressed to Mohan Rajesh Elara; gs.ude.dtus@aralehsejar

Received 30 January 2019; Revised 30 May 2019; Accepted 23 July 2019; Published 12 September 2019

Academic Editor: Antonio Concilio

Copyright © 2019 Balakrishnan Ramalingam et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Aircraft surface inspection includes detecting surface defects caused by corrosion and cracks and stains from the oil spill, grease, dirt sediments, etc. In the conventional aircraft surface inspection process, human visual inspection is performed which is time-consuming and inefficient whereas robots with onboard vision systems can inspect the aircraft skin safely, quickly, and accurately. This work proposes an aircraft surface defect and stain detection model using a reconfigurable climbing robot and an enhanced deep learning algorithm. A reconfigurable, teleoperated robot, named as “Kiropter,” is designed to capture the aircraft surface images with an onboard RGB camera. An enhanced SSD MobileNet framework is proposed for stain and defect detection from these images. A Self-filtering-based periodic pattern detection filter has been included in the SSD MobileNet deep learning framework to achieve the enhanced detection of the stains and defects on the aircraft skin images. The model has been tested with real aircraft surface images acquired from a Boeing 737 and a compact aircraft’s surface using the teleoperated robot. The experimental results prove that the enhanced SSD MobileNet framework achieves improved detection accuracy of aircraft surface defects and stains as compared to the conventional models.