航空发动机涡轮叶片表面缺陷内窥视觉SH+YOLOv8检测识别研究

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关键词：航空发动机涡轮叶片;表面缺陷检测;YOLOv8;可切换空洞卷积;特征融合中图分类号：TB9;TN98;TM75;TP391.41 文献标志码：A 文章编号：1674-5124（2026）02-0121-07

Abstract： Surface defects ofaero-engine turbine blades （ATB） directly afect the operational safety and service lifeof engines.Traditional manual inspection methods sufer from unstable accuracy，low eficiency，and high labor intensity.To address these issues，this paper proposes a detection and recognition method for ATB surface defects by combining SH （switchable atrous convolution + high-level screening-feature fusion pyramid networks） with YOLOv8. In the backbone network，the SAConv module is introduced to enhance multi-scale feature extraction， while the HS-FPN module is embedded in the neck network to improve feature fusion and defect representation. Experiments are conducted on a self-constructed borescope image dataset. The results show that the improved SH+YOLOv8 model achieves an F1-score of 0.93 and mAP（∅0.5 of 94.8% ，witha （204号 0.6% improvement in mAP（∅0.5 compared to the original YOLOv8， meeting the accuracy requirements of engineering applications.The findings demonstrate that the proposed method exhibits good adaptability and robustness under complex conditions， providing valuable insights for intelligent detection of key aero-engine components.

Keywords： aro-engine turbine blades; surface defect detection; YOLOv8; switchable atrous convolution; feature fusion

0 引言

航空发动机涡轮叶片（aero-engine turbineblades，ATB）是航空发动机重要部件之一，长期运载容易出现烧伤、缺口、凹坑等表面缺陷，直接影响飞行安全[1]。（剩余11295字）