Abstract
Visual investigation methods are conventionally applied during structural integrity inspection and defect detection. Inaccessible zones and field inspection restrictions are applicable to tall buildings, large bridges, dams, power plants as well as large commercial airliners. Through the fast technological progress of unmanned aerial vehicle (UAV) and recent advances in digital image processing techniques, conventional visual inspection could soon become obsolete. A defect detection approach utilising the advanced UAV and digital image processing techniques is proposed in this paper. This study is part of a bigger research investigating into the ability of an advanced UAV to autonomously traverse over an aircraft and accurately detect locations of defects on the aircraft skin to assist in further investigation of subsurface defects utilizing thermal and ultrasound techniques. The main aim of the research study is to minimize the inspection time of a commercial aircraft to the order of an hour.
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References
Afzal, A., AlDhafari, L., AlManai, A., Akbari, A.S., Miah, MD.S., Hossain, M.S. (2023) Conceptual design of an autonomous unmanned arial vehicle, Proc. of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME 2023), pp. 1655–1659, 19–21 July 2023, Tenerife, Canary Islands, Spain.
AlBahrani, R. (2023) Development of image processing techniques in crack detection and analysis, BEng Thesis, Military Technological College, Muscat, Oman.
AlManai, A. (2023) Design and development of an autonomous unmanned aerial vehicle, BEng Thesis, Military Technological College, Muscat, Oman.
Civil Aviation Safety Authority Australia, Airworthiness Bulletin. https://www.casa.gov.au/files/awb-indexpdf, (2019).
Dalton, R., Cawley, P., and Lowe, M. The potential of guided waves for monitoring large areas of metallic aircraft fuselage structure. Journal of Nondestructive Evaluation, 20, pp. 29-46 (2001).
Gunatilake, P., Siegel, M., Jordan, A. G., and Podnar, G. W. Image enhancement and understanding for remote visual inspection of aircraft surface. In Proc. SPIE 2945, Nondestructive Evaluation of Aging Aircraft, Airports, and Aerospace Hardware, Scottsdale, AZ, USA, (1996).
Gunatilake, P., Siegel, M., Jordan, A. G., and Podnar, G. W. Image understanding algorithms for remote visual inspection of aircraft surfaces. In Proc. SPIE 3029, Machine Vision Applications in Industrial Inspection V, pp. 2–14, San Jose, CA, USA, (1997).
Hussey, Tyler B. Surface Defect Detection in Aircraft Skin & Visual Navigation based on Forced Feature Selection through Segmentation. Theses and Dissertations. https://scholar.afit.edu/etd/5044, (2021).
Kim, B. and Cho, S. Automated vision-based detection of cracks on concrete surfaces using a deep learning technique. Sensors, 18(10), 3452; https://doi.org/10.3390/s18103452, (2018).
JovaňcevÃc, I., Larnier, S., Orteu, J.-J., and Sentenac, T. Automated exterior inspection of an aircraft with a pan-tilt-zoom camera mounted on a mobile robot. Journal of Electronic Imaging, 24(6). https://doi.org/10.1117/1.JEI.24.6.061110, (2015).
Li, Y., Huang, H., **e, Q., Yao, L., and Chen, Q. Research on a surface defect detection algorithm based on MobileNet-SSD. Applied Sciences, 8(9), 1678; https://doi.org/10.3390/app8091678, (2018).
Liwauddin, M.L., Ayob, M.A., Zakaria, M.F., Shamsudin, A.U. and Rohaziat, N. Development of A Dodecacopter using Pixhawk 2.4.8 Autopilot Flight Controller, (2022).
Luukkonen, T. Modelling and control of quadcopter. Independent research project in applied mathematics, Espoo, 22(22) (2011).
Lee, Y. Literature review on edge detection methods, Indiana: Methods for Research and Dissemination, Earlham College (2020).
Tao, X., Zhang, D., Ma, W., Liu, X., and Xu, D. Automatic metallic surface defect detection and recognition with convolutional neural networks. Applied Sciences, 8(9), 1575; https://doi.org/10.3390/app8091575, (2018).
Mumtaz, R., Mumtaz, M., Bin Mansoor, A. & Masood, H. omputer Aided Visual Inspection of Aircraft Surfaces. International Journal of Image Processing (IJIP), 6(1), pp. 38–53 (2012).
Ortiz A, Bonnin-Pascual F, Garcia-Fidalgo E, Company-Corcoles JP. Vision-based corrosion detection assisted by a micro-aerial vehicle in a vessel inspection application. Sensors. 16(12):2118 (2016).
Acknowledgements
The authors are grateful for the financial support by the Ministry of Higher Education Research and Innovation (BFP/RGP/EI/22/217) Oman.
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Hossain, M.S., Afzal, A., AlDhafari, L.S., AlManai, A., AlBahrani, R.I. (2024). Development of an Autonomous Unmanned Aerial Vehicle for Rapid Aircraft Defect Detection. In: Khan, A.A., Hossain, M.S., Fotouhi, M., Steuwer, A., Khan, A., Kurtulus, D.F. (eds) Proceedings of the First International Conference on Aeronautical Sciences, Engineering and Technology . ICASET 2023. Springer, Singapore. https://doi.org/10.1007/978-981-99-7775-8_9
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