# Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5
## 1 Introduction  
As the core of aircraft, aero engines work in harsh environments involving high temperature, high pressure and high load over long periods of time, their components are subjected to aerodynamic force from flowing gas and they are impacted by foreign objects. Cracks, gaps, burns, pits and other damage thus occur frequently [1]. The consequences of such defects can be fatal and the financial costs very high. Therefore, it is very important to detect defective aero-engine components in time and ensure the flight safety of aircraft. Defect detection is a necessary task for the extension of the service lives of these parts, as replacing them is far more expensive [2]. At present, the methods for surface defect detection of aero-engine components generally include the borescope inspection, magnetic powder, ray, penetration, eddy current and ultrasonic methods. These methods have achieved good performances for detection of aero-engine components, but many defects are too small to detect with these methods, and detection work mainly relies on experienced inspectors, who perform intensive work and can easily miss tiny defects [3]. There is thus an urgent need for computer vision detection methods that can replace manual methods. In recent years, with the development of deep learning, computer vision technology has been applied in many fields, such as face recognition, object detection and automatic driving. Object detection algorithms based on computer vision are generally divided into two categories: two-stage and one-stage algorithms. Two-stage algorithms are based on the candidate regions and include R-CNN [4], Fast R-CNN [5] and Faster R-CNN [6]. One-stage algorithms can directly obtain the position and category probability of the object, and they include YOLO [7] and SSD [8]. Recent research has shown that theVision Transformer model (ViT) can achieve comparable or even superior performance in image classification tasks, as it uses self-attention rather than convolution to aggregate information across locations [9]. [PERSON] et al. used random fields and likelihood ratios to analyze the development of convolutional neural networks intended for use in solving recognition problems, as well as metrics designed to assess the quality and reliability of object detection [10]. [PERSON] et al. provided a complete view of unsupervised anomaly detection for high dimensional data and proposed a hybrid framework to produce an unsupervised anomaly detection algorithm [11]. [PERSON] et al. showed that reliance on CNNs is unnecessary and proved that a pure transformer directly applied to sequences of image patches can perform very well in image classification tasks, attaining excellent results compared to the most advanced CNNs, while the ViT model required substantially fewer computational resources to train [12]. Many scholars have been working to combine object detection algorithms with practical application scenarios and have achieved significant results. [PERSON] et al. designed an anchor-free feature selection mechanism and a dense convolution structure and introduced them into the YOLOv3 network to improve the detection speed and accuracy of the algorithm in detecting steel strip surface defects [13]. [PERSON] et al. proposed an intelligent system for the estimation of the spatial positions of apples based on YOLOv3 and a D415 RealSense Depth Camera, which obtained the position estimates of the apples with high accuracy in a symmetric coordinate system [14]. [PERSON] et al. undertook a comprehensive analysis of modern object detection models that can be used for defect detection applications in industry and analyzed the applicable detection models under different leading constraints, providing an important reference for industrial defect detection [15]. Aiming at the problems of complex architecture and low detection accuracy in traditional aero-engine sensor fault detection algorithms, [PERSON] et al.