Aircraft skin machine learning-based defect detection and size estimation in visual inspections

Abstract

Aircraft maintenance is a complex process that requires a highly trained, qualified, and experienced team. The most frequent task in this process is the visual inspection of the airframe structure and engine for surface and sub-surface cracks, impact damage, corrosion, and other irregularities. Automated defect detection is a valuable tool for maintenance engineers to ensure safety and condition monitoring. The proposed approach is to process the captured feedback using various deep learning architectures to achieve the highest performance defect detections. Additionally, an algorithm is proposed to estimate the size of the detected defect. The team collaborated with TUI’s Airline Maintenance Team at Luton Airport, allowing us to fly a drone inside the hangar and use handheld cameras to collect representative data from their aircraft fleet. After a comprehensive dataset was constructed, multiple deep-learning architectures were developed and evaluated. The models were optimized for detecting various aircraft skin defects, with a focus on the challenging task of dent detection. The size estimation approach was evaluated in both controlled laboratory conditions and real-world hangar environments, providing insights into practical implementation challenges.