| Citation: | JIA Lu, ZENG Hao, PENG Jingpo, et al. Aero-engine blade damage detection based on data augmentation and multi-scale fusion[J]. Journal of Aerospace Power, 2025, 40（10）：20250155 doi: 10.13224/j.cnki.jasp.20250155
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To address high missed detection rates for small-sized damages and significant environmental variations in aero-engine blade inspection during service-induced damage, an enhanced YOLOv5-based detection method was developed. A data augmentation strategy integrating random affine transformation, HSV space enhancement, and bidirectional flipping was proposed, which effectively mitigated sample imbalance across damage categories. Second, a multi-kernel adaptive convolution module and multi-scale feature aggregation module were introduced to improve network capability in extracting fine-grained damage features and discriminating subtle characteristics, effectively addressing the technical bottlenecks of insufficient detection performance for small-sized damages with indistinct morphological features. The experimental results showed that compared with the baseline model, the detection accuracy (mAP@0.5) of the improved model increased by 16.8%. In the comparison, the detection accuracy of the improved model was higher than that of mainstream object detection frameworks such as Faster-RCNN, SSD, Deformable DETR, YOLOv8, YOLOv11, and YOLOv12. Furthermore, the proposed method exhibited 13.9% and 15.3% improvements in mAP@0.5 over existing defect detection models SW-YOLO and YOLOv8-EMA, respectively.
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