Artificial Intelligence and Computer Vision for Automated Aircraft Defect Detection
TL;DR
Validation at demonstrator scale confirmed the system’s ability to meet the initially defined specifications, demonstrating reliable detection of both defects and organic residues, including those caused by impacts and insect accumulation on aircraft surfaces.
Abstract
The inspection of aeronautical components requires compliance with extremely high safety and quality standards, where early detection of defects and verification of surface conditions are critical for aircraft reliability. The use of robots equipped with computer vision enables the automation of these inspections, improving coverage, consistency, and safety compared to manual methods, while enhancing overall operational efficiency. The incorporation of artificial intelligence further enhances these systems by enabling automatic defect detection, classification according to severity, and real-time inspections, reducing human errors and streamlining the inspection process. This work presents a technically viable solution not only for defect detection but also for evaluating the cleanliness or contamination of aeronautical surfaces, using non-destructive methods based on computer vision, profilometry, and artificial intelligence, grounded in image acquisition and analysis. Validation at demonstrator scale confirmed the system’s ability to meet the initially defined specifications, demonstrating reliable detection of both defects and organic residues, including those caused by impacts and insect accumulation on aircraft surfaces. This approach combines automation, precision, and traceability, representing a significant step forward toward advanced and safe inspection practices in the aerospace industry.