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

• Published in: International journal of aerospace engineering Vol. 2019; no. 2019; pp. 1 - 14
• Main Authors: James, Tan Jun Yuan, Lakshmanan, Anirudh Krishna, Vengadesh, Ayyalusami, Mohan, Rajesh Elara, Manuel, Vega-Heredia, Ramalingam, Balakrishnan, Ilyas, Muhammad
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2019; Hindawi; Hindawi Limited
• Subjects: Aerospace engineering; Aircraft; Aircraft detection; Aircraft industry; Algorithms; Architectural engineering; Artificial intelligence; Automation; Climbing; Concrete; Cracks; Deep learning; Dirt; Flaw detection; Human performance; Image acquisition; Image detection; Image enhancement; Image filters; International conferences; Localization; Machine learning; Model testing; Morphology; Neural networks; Oil spills; Onboard equipment; Reconfiguration; Repair & maintenance; Robotics; Robots; Sediments; Skin; Surface defects; Unmanned aerial vehicles; Vision systems
• ISSN: 1687-5966; 1687-5974
• DOI: 10.1155/2019/5137139

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.