Deep-Learning-Based Defect Detection for Light Aircraft With Unmanned Aircraft Systems

Abstract

Visual inspections of aircraft are a vital aspect of aircraft maintenance. They involve ground personnel walking around the aircraft, mounting scaffolding or ladders, and fully inspecting the aircraft. A visual inspection, however, requires a significant amount of time to perform and may be prone to human error. To address these challenges, a deep-learning-based defect-detection model is proposed, which has the potential to reduce inspection time significantly and enhance inspection reliability. This approach utilises an Unmanned Aircraft System (UAS) equipped with an onboard camera system to capture images for analysis. The proposed machine-learning (ML) model is trained using a deep-learning framework, specifically YOLOv8, on a diverse dataset comprising of both defective and non-defective images of the aircraft. The labelling process for novel classes is accelerated through utilising a low accuracy pre-trained model. Following model analysis, the operator inspects the defects reported in the images. To facilitate efficient identification, object detection techniques, such as bounding boxes, are employed. In this study, various hyperparameter combinations to optimise the model's performance are explored. Implementing this ML detection system would mitigate the risks associated with human error and improve inspection accuracy, providing a safer visual inspection process for ground personnel and a more efficient solution for aircraft visual inspections for preventative maintenance. Moreover, the utilisation of a UAS enables inspections of inaccessible or hazardous areas without compromising personnel safety. This research demonstrates the potential of deep learning for aircraft visual inspections. The defects analysed are damaged and missing rivets, filiform corrosion, and missing panels, while future research aims to expand the scope to include additional defects such as paint chipping, scratches, burns, and rust. The model's capability to discern the condition of rivets, panels, and filiform corrosion is evidenced by its achieved validation mean Average Precision (mAP) of 85 %.