Advances in Aircraft Skin Defect Detection Using Computer Vision: A Survey and Comparison of YOLOv9 and RT-DETR Performance

Abstract

Aircraft skin surface defect detection is critical for aviation safety but is currently mostly reliant on manual or visual inspections. Recent advancements in computer vision offer opportunities for automation. This paper reviews the current state of computer vision algorithms and their application in aircraft defect detection, synthesizing insights from academic research (21 publications) and industry projects (18 initiatives). Beyond a detailed review, we experimentally evaluate the accuracy and feasibility of existing low-cost, easily deployable hardware (drone) and software solutions (computer vision algorithms). Specifically, real-world data were collected from an abandoned aircraft with visible defects using a drone to capture video footage, which was then processed with state-of-the-art computer vision models — YOLOv9 and RT-DETR. Both models achieved mAP50 scores of 0.70–0.75, with YOLOv9 demonstrating slightly better accuracy and inference speed, while RT-DETR exhibited faster training convergence. Additionally, a comparison between YOLOv5 and YOLOv9 revealed a 10% improvement in mAP50, highlighting the rapid advancements in computer vision in recent years. Lastly, we identify and discuss various alternative hardware solutions for data collection — in addition to drones, these include robotic platforms, climbing robots, and smart hangars — and discuss key challenges for their deployment, such as regulatory constraints, human-robot integration, and weather resilience. The fundamental contribution of this paper is to underscore the potential of computer vision for aircraft skin defect detection while emphasizing that further research is still required to address existing limitations.