Real-Time Detection of Aircraft Surface Damages Using UAV-Based Aerial Imaging with YOLOv8

The evaluation and walk-around check before an aircraft flight are essential for ensuring aircraft safety. This paper describes a method for creating a system capable of detecting and identifying damages on aircraft surfaces using imagery captured from UAV-based aerial platforms, achieving real-time Internet of Things (IoT) monitoring via other devices. Our aim in this project is to detect skin damage, such as scratches, cracks, and dents, that pose significant threats to the structural integrity and safety of aircraft. Traditional inspection methods are often time-consuming and labor-intensive, making real-time detection systems essential for timely maintenance and safety assurance.The system utilizes UAVs to capture high-resolution aerial images of aircraft surfaces. These images undergo processing by trained machine learning algorithms to detect and classify desired objects in real time at the ground station. The results of the image processing can then be monitored via IoT devices. Experimental results demonstrate the system's effectiveness and efficiency in identifying skin damage on aircraft materials. However, certain limitations have emerged, including restricted coverage of defect types, reduced accuracy with increased class numbers, and substantial hardware requirements. Despite these shortcomings, the system remains promising for enhancing aircraft safety through proactive maintenance and defect detection.