An edge-guided defect segmentation network for in-service aerospace engine blades

Abstract

Currently, 80 % of in-service aerospace engine blade defect detection relies on manual visual assessment. Operators use a borescope to capture images of the blade surface and make judgments based on their experience and expertise. However, this method is costly and time-consuming. With the widespread application of artificial intelligence across various fields, its strong capabilities in automated defect detection are becoming increasingly evident. To meet the demand for efficient defect detection in aero-engine blades, we have constructed a dataset based on videos collected from real inspection scenarios, ensuring alignment with actual defect types.

Based on this dataset, we analyze existing defect detection methods for in-service aero-engine blades and propose an improved edge-guided and channel-enhanced network using the "Transformer" architecture. Our method leverages global edge information from "Segment Anything (SAM)" to guide learning, while the channel shuffling module boosts feature capture. Experimental results show an mean intersection over union (mIoU) of 88.13 % and a detection speed of 30.6 frames per second (FPS) on a single graphics processing unit (GPU), meeting real-world efficiency needs. The code will be publicly available at the link: https://github.com/Newbiejy/EGCIENet_In-service-blade-defect-detection.