Fine-grained classification for aero-engine borescope images based on the fusion of local and global features

Borescope image classification is an essential preprocessing step for damage detection in aero-engines, which plays a crucial role in setting the detection threshold for later stages. However, traditional image classification methods such as ResNet or fine-grained image classification methods like FBSD perform poorly in classifying borescope images. To address this issue, we propose a novel and effective fine-grained classification method for aero-engine borescope images based on the fusion of local and global features. Our approach leverages a region proposal network to identify key local regions and combines the local features with global features for better classification. This enables us to distinguish between regions with high similarity and small inter-class differences. Additionally, we have created an aero-engine borescope image dataset with 5,158 images from 14 aero-engine components. Our method achieves a high classification accuracy of 98.31% on this dataset, outperforming other image classification and fine-grained image classification methods. Our implementation code is available at https://github.com/LonelyProceduralApe/LG_fusion