# Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5
## 1 Introduction  
As the core component of an aircraft, the stability of the aircraft engine is a prerequisite for safe operation, and its performance is also a key factor determining the technical performance and economic benefits of the aircraft. Currently, most aircraft use turbofan engines to generate thrust, which consist of three main components: the compressor, the combustion chamber, and the turbine. These critical components work in harsh environments of high temperature, high pressure, and high load for long periods of time, enduring significant mechanical and thermal stresses. Due to the extreme working conditions and continuous operation they undergo, these components are prone to defects and damage such as cracks, scratches, pits, and corrosion [1]. These damages not only affect the performance of aircraft engines but also pose significant safety hazards to flight, potentially leading to serious accidents. In recent years, the frequency of flight accidents caused by issues such as blade strikes and combustion chamber cracks has been increasing. Therefore, the timely detection and repair of defective or damaged components are crucial to ensure the reliability and stability of aircraft in various working environments, and they are essential for flight safety. To ensure the reliability and performance of aircraft engines, aircraft manufacturers and airlines employ strict inspection procedures. Conventional inspection methods mainlyinvolve visual inspections using borescopes, wherein engineers use borescopes to detect and assess defects and damages in internal engine components, and then take appropriate repair measures. This method has achieved good performance in the detection of aircraft engine components. However, the internal structure of aircraft engines is complex, and the effectiveness of the detection work mainly relies on the experience of operators. Moreover, long hours and high-intensity work can lead to visual fatigue and the potential for over-looking small defects [2]. Therefore, there is an urgent need for new technologies to replace traditional detection methods. With the continuous advancement of technology, new inspection techniques are emerging. In recent years, with the rapid development of computer technology, computer vision has been widely applied in various fields, and object detection is an important part of it. Compared to traditional manual visual inspection, computer vision-based techniques significantly reduce labor costs and offer advantages such as stability, high efficiency, and accuracy [3]. To improve the efficiency of aircraft engine component inspection and reduce inspection costs, object detection techniques can be applied to the surface defect detection of aircraft engine components, achieving automated and efficient inspection. Currently, object detection techniques can be categorized into two main types: one-stage algorithms and two-stage algorithms. The difference between them lies in their detection processes. One-stage object detection algorithms include YOLO [4] (You Only Look Once) series, SSD [5] (Single Shot MultiBox Detector), EfficientDet [6], and others. These algorithms treat object detection as a regression problem and directly predict the position and category of bounding boxes through convolutional neural networks. Their detection process usually requires only one forward pass. Two-stage object detection algorithms include R-CNN [7] (Region-based Convolutional Neural Network) series, Fast R-CNN [8], Faster R-CNN [9], Mask R-CNN [10], and others. These algorithms first extract region proposals and then classify and regress locations for each candidate box to obtain the final detection results. [PERSON] et al. [11] conducted a comprehensive analysis of modern object detection models, which can be applied to industrial defect detection applications. They also analyzed the application of applicable detection models under different main constraints, providing important references for industrial defect detection. [PERSON] et al.