关键词: 飞机机翼, 腐蚀识别, 深度学习, 小样本, 迁移学习

Abstract: In the harsh corrosive environments characterized by high temperatures,high humidity and high salt spray,corrosion issues frequently arise in the structures of aircraft wings and fuselage.Rapid and accurate detection of surface metal corrosion,along with the implementation of appropriate repair and maintenance measures was crucial for ensuring flight safety,extending service life and reducing aircraft downtime for maintenance.The characteristics of metal corrosion on aircraft wing surfaces,highlighting typical corrosion sites and types were analyzed.Through a review and analysis of relevant literature from both domestic and international sources,the visual detection and identification methods for detecting metal corrosion on aircraft wings were discussed.The current state of research was summarized across three key approaches:traditional image processing,machine learning and deep learning.Particular emphasis was placed on deep learning-based corrosion identification methods,with an analysis of the principles and applications of techniques such as YOLO and Detection Transformer (DETR).To address the issue of insufficient datasets for aircraft wing surface metal corrosion that negatively impacts model training performance,the sample augmentation methods based on data enhancement and transfer learning were explored,and the relevant datasets and performance evaluation metrics for corrosion identification were compiled.Finally,the key challenges in identifying metal corrosion on aircraft wing surfaces and their corresponding solutions were summarized,while providing insights and predictions for future research directions and trends in this field.

Key words: aircraft wings, corrosion identification, deep learning, small sample size, transfer learning