UAV-supported visual inspection of aircraft for corrosion and crack detection

Abstract Maintenance procedures are critically important for preserving the structural integrity, maintaining the functionality and ensuring the operational safety of aircraft. Traditional inspection techniques used in aircraft are often costly, time-consuming and prone to human mistake. Today, the opportunities provided by digitalisation and automation in aircraft maintenance and inspection processes are paving the way for innovative approaches. In this context, the use of inspection systems supported by image processing technologies has the potential to bring about a significant transformation in aircraft maintenance. Visual inspection methods integrated with unmanned aerial vehicles (UAVs) enable the rapid, accurate and repeatable detection of defects such as corrosion and cracks on the external surfaces of aircraft. This study focuses on the automatic detection and classification of defects on the external surfaces of aircraft, based on tests and analyses carried out by artificial intelligence algorithms using high-resolution data. The model developed in this study was implemented in Python in the Google Colab environment and supported by AI algorithms trained on visual data. The main objective is to investigate the feasibility of UAV-based systems for aircraft visual inspection and to provide concrete evidence of their practical applicability. In this regard, the UAV platform selected for image acquisition is intended to comprehensively scan the target areas and capture images with sufficient resolution for processing by artificial intelligence algorithms. A review of the literature reveals that UAV- and AI-based integrated approaches have been explored in only a limited number of studies related to aircraft maintenance. In this context, the present study proposes a system that enables the rapid and accurate detection of structural defects such as corrosion and cracks on the external surfaces of aircraft.