A lightweight vision-based framework for automated aero-engine blade damage inspection

Automated visual inspection of aero-engine blades is central to aviation safety, yet it is challenged by damage that is small and low-contrast against a strongly textured metal surface, and by the need for models that are light enough for edge deployment. This paper presents a lightweight vision-based framework for automated blade damage inspection. Rather than assembling independent detection modules, the framework is organized as a three-stage inspection pipeline: (1) a multi-scale feature-discrimination stage (MSBlock) captures defects across a wide size range at negligible computational cost; (2) a texture-suppression stage (TSA) filters high-frequency machining-background noise through product-based channel-spatial attention, exposing genuine defects; and (3) an efficient-prediction stage (Detect_Efficient) fuses cross-scale features and recalibrates confidence with roughly half the parameters of a standard head. On a self-built engine-blade dataset, the framework improves recall, F1-score, and mAP@0.5 by 4.8, 1.9, and 0.8 percentage points over YOLOv11n (reaching 92.0%, 91.6%, and 94.2%), while cutting parameters and computation by 50.0% and 58.7% and reaching 527 FPS. It also outperforms YOLOv8n, YOLOv9t, and YOLOv10n in recall and F1-score, confirming its suitability for real-time edge-based blade inspection.