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<p>Reliable detection of aero-engine blade surface defects is hindered by weak defect saliency, long-tailed category imbalance, and strong geometric priors from curved surfaces. This paper proposes MSFF-YOLO, a multi-scale feature fusion framework built upon YOLOv11. The method integrates a Multi-scale Efficient Aggregation Module (MEAM) for enhancing subtle and edge-attached defects, a multi-scale FMSIoU loss for improving regression robustness under long-tailed distributions, and a Manhattan Self-Attention (MaSA) mechanism for modeling curvature-related spatial dependencies. Evaluated on the high-resolution AeBSDD dataset, MSFF-YOLO achieves an <i>mAP₅₀</i> of 89.1%, surpassing YOLOv11 especially on nick, bent, and dent defects. Real-world illumination-disturbance tests and zero-shot evaluation on NEU-DET further verify its strong cross-scene and cross-domain generalization, demonstrating its robustness for industrial blade inspection.</p>

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MSFF-YOLO: A Multi-Scale Feature Fusion Network for Aero-Engine Blades Surface Defects Detection

  • 期刊论文

  • 发表日期：

    2026年1月19日

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  • Jiaqi Yuan,
  • Lixiang Zhao,
  • Wenguang Ye,
  • Yunyong Cheng,
  • Wenfeng Cai

摘要

Reliable detection of aero-engine blade surface defects is hindered by weak defect saliency, long-tailed category imbalance, and strong geometric priors from curved surfaces. This paper proposes MSFF-YOLO, a multi-scale feature fusion framework built upon YOLOv11. The method integrates a Multi-scale Efficient Aggregation Module (MEAM) for enhancing subtle and edge-attached defects, a multi-scale FMSIoU loss for improving regression robustness under long-tailed distributions, and a Manhattan Self-Attention (MaSA) mechanism for modeling curvature-related spatial dependencies. Evaluated on the high-resolution AeBSDD dataset, MSFF-YOLO achieves an mAP₅₀ of 89.1%, surpassing YOLOv11 especially on nick, bent, and dent defects. Real-world illumination-disturbance tests and zero-shot evaluation on NEU-DET further verify its strong cross-scene and cross-domain generalization, demonstrating its robustness for industrial blade inspection.