基于SW-YOLO模型的航空发动机叶片损伤实时检测  

Real time detection of aircraft engine blade damage based on SW-YOLO model

作　　者：何宇豪 曹学国 刘信良 蒋浩坤 王静秋[1] HE Yuhao;CAO Xueguo;LIU Xinliang;JIANG Haokun;WANG Jingqiu(National Key Laboratory of Science and Technology on Helicopter Transmission,Nanjing University of Aeronautics and Astronautics,Nanjing 210016,China;Engine Division of Engineering Department,Guangzhou Aircraft Maintenance Engineering Co.Ltd.,Guangzhou 510470,China)

机构地区：[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

出　　处：《推进技术》2024年第2期192-198,共7页Journal of Propulsion Technology

基　　金：直升机传动技术重点实验室基金(HTL-A-21G03)。

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P_(A)@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。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 P_(A)@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.

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 

分 类 号：V263.6[航空宇航科学与技术—航空宇航制造工程] TP391.4[自动化与计算机技术—计算机应用技术]