Research On The Blade Crack Detection Of Aero-engine Based On Image Recognition

Aero-engine blades are important components of aero-engine and their normal operation can provide continuous flight power for the engine.They work in high temperature and high pressure environment and the working time is often very long.Such a poor environment tends to cause fatigue cracks in the engine blades,and these cracks on these engine blades will pose a potential threat to the normal operation of the aircraft engine.In fact,as long as there are cracks on the engine blades,regardless of their size,they will endanger the personnel and pose a serious threat to the machine and even cause irreparable damage.Therefore,there is an urgent need for an intelligent and efficient way to detect aero-engine blade cracks,which has important safety significance.The main work of the thesis is as follows:(1)The R-FCN(Object Detection via Region-based Fully Convolutional Networks)algorithm is applied to the detection task of aero-engine blade cracks,and then the position-sensitive score maps and the position-sensitive ROI pooling of R-FCN algorithm is used to ensure that this neural network can distinguish the category of the object to be detected and determine its location accurately.The improvement of this algorithm mainly considers the fine and long characteristics of the aero-engine blade crack itself,and this thesis improves the anchor part of the Region Proposal Network(RPN)in the R-FCN algorithm to improve detection accuracy.In addition,after adding noise to the image test sets of the aero-engine blades,the experimental results show that the improved R-FCN algorithm is robust to blade crack images containing noises.(2)The YOLOv3(You Only Look Once)algorithm is applied to the detection task of aero-engine blade cracks.The YOLO series of algorithms is a typical representative algorithm based on the one-stage method,and its detection speed is very fast.This thesis draws on the multi-scale and multi-level detection structure of the FPN(Feature Pyramid Networks)and improves this pyramid structure in the initial YOLOv3.It constructs a pyramid structure with more feature scales and more levels,and then it fully merges high-level high-semantic feature maps with low-level high-resolution feature maps.Finally,the fused feature map is used as the prediction layer to predict the object and achieve multi-scale prediction to obtain more information about the characteristics and location of small objects.Through experimental comparison and analysis,the improved algorithm has better detection accuracy than before,and it can achieve real-time detection.In addition,after adding noise to the image test sets of the aero-engine blades,the experimental results show that the improved YOLO algorithm is less robust to blade crack images containing noises.