영상처리와 YOLO 기반 딥러닝을 활용한 항공기 WAI 결함 검출

초록

In the aviation industry, early detection of external defects is essential for flight safety and maintenance efficiency. Traditional Walk Around Inspection(WAI), which depends on human vision, is vulnerable to errors caused by lighting, weather, and inspector variability. To address these limitations, this study evaluates the applicability of deep learning-based object detection models for automating defect detection. Specifically, YOLOv5, YOLOv8, and YOLOv9 were trained and tested under identical conditions using two datasets: a public aircraft defect dataset from Roboflow and a custom-built dataset comprising 30 riveted aluminum panels that simulate real-world fuselage defects. Model performance was assessed using Precision, Recall, and mAP@0.5. Among the three, YOLOv9 achieved the highest accuracy across all metrics, followed by YOLOv8 and YOLOv5. These results demonstrate the effectiveness of YOLO-based models for detecting aircraft surface anomalies and support their potential for integration into automated inspection workflows in real maintenance environments.

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