引用本文:
【打印本页】   【HTML】 【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览 22次   下载 16 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于SW-YOLO模型的航空发动机叶片损伤实时检测
何宇豪1,曹学国2,刘信良1,蒋浩坤1,王静秋1
1.南京航空航天大学 直升机传动技术重点实验室,江苏 南京 210016;2.广州飞机维修工程有限公司 工程部发动机处,广东 广州 510470
摘要:
孔探检测技术是航空发动机叶片损伤检测的主要手段,但目前依赖人工操作,耗时耗力。本文提出了一个孔探视频检测的SW-YOLO模型,该模型包括输入端、主干网络、颈部网络、头部网络4个模块。首先,在主干网络加入了空间通道注意力模块(Spatial Channel-Convolutional Block Attention Module,SC-CBAM),有效避免位置信息丢失,提高目标边界回归能力,相较于YOLOv5,其平均精度均值PˉA@0.5提高了5.4%。其次,在颈部网络对特征金字塔网络(Feature Pyramid Network,FPN)进行了改进,通过融合低层特征,扩大了模型感受野,有利于较小损伤区域的检测,如烧蚀损伤,平均精度提高了8.1%。最后,通过与YOLOv5,Faster R-CNN,SSD模型的对比实验,结果表明SW-YOLO模型的平均精度均值分别提高了7%,6.2%,6.3%,检测速度满足实时检测需求,有利于提高航空发动机孔探检测的自动化和智能化水平。
关键词:  航空发动机  叶片损伤  深度学习  孔探检测  目标检测  实时检测
DOI:10.13675/j.cnki.tjjs.2302058
分类号:V263.6;TP391.4
基金项目:直升机传动技术重点实验室基金(HTL-A-21G03)。
Real time detection of aircraft engine blade damage based on SW-YOLO model
HE Yuhao1, CAO Xueguo2, LIU Xinliang1, JIANG Haokun1, WANG Jingqiu1
1.National Key Laboratory of Science and Technology on Helicopter Transmission,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;2.Engine Division of Engineering Department,Guangzhou Aircraft Maintenance Engineering Co. Ltd., Guangzhou 510470,China
Abstract:
Borescope detection technology is one of the main means for detecting damage of aero-engine blades, but currently it mainly relies on manual operation and is time-consuming and labor-intensive. This paper proposes a SW-YOLO model for aero-engine blade damage borescope video detection. The model includes 4 modules: input terminal, backbone network, neck network and head network. Firstly, by adding a space channel attention module Spatial Channel-Convolutional Block Attention Module (SC-CBAM) to the backbone network to alleviate the loss of location information and improve the ability of target boundary regression, and its average accuracy PˉA@0.5 increases by 5.4% compared with YOLOv5. Secondly, the structure of Feature Pyramid Network (FPN) is improved in the neck network, and the low-level features are fused to expand the receptive field of the model, which has a better detection effect for the smaller damage area, such as ablation, and the average accuracy is improved by 8.1%. At last, compared with YOLOv5, Faster R-CNN and SSD models, the experimental results show that the average precision mean of the SW-YOLO model has been improved about 7%, 6.2%, 6.3%, respectively, and the detection speed meets the real-time detection requirements, which is conducive to improving the automation and intelligence level of aero-engine blade damage borescope detection.
Key words:  Aircraft engine  Blade damage  Deep learning  Borescope detection  Object detection  Real-time detection