## 1 Introduction  
Identifying crack defects during the inspection of aging aircraft is of vital importance to the safety of the aircraft. The main reason for the China Airlines Flight 611 [1] air crash was the presence of cracks, which had not been completely flattened and became more and more serious.. They eventually caused the body of the planeto disintegrate in mid-air. This paper focuses on the detection of cracks in the aircraft structures, such as their engines or fuselage surface. The method used at present for aircraft crack detection is visual inspection. This method involves a great amount of human labor, during which technicians must be fully focused in order to accurately find all the damaged areas. With the eruption in development of airlines in recent years, traditional visual inspection methods cannot fulfill the vast demand, as well as the high accuracy requirements, for crack inspection, due to the possibility of missing or false detection caused by human fatigue. Therefore, an automatic and intelligent method which can extract crack information from images or videos is necessary in order to reduce human labor and to help servicing engineers to speed up the inspection process and improve accuracy simultaneously. Therefore, the study of aircraft crack detection has attracted the interest of many researchers. These works will be illustrated in two aspects. One is the traditional detection method, which depends on special hardware facilities, and the other is the visual detection method, based on deep learning. The first kind of crack detection methods depends on specialized hardware devices. A resonant ultrasound spectroscopy apparatus was provided for detecting crack-like flaws in components in [2]. [PERSON] et al. applied an aircraft structural health-monitoring system to detect damaged areas in a full-scale aircraft fatigue tests [3]. [PERSON] et al. [4] used a self-diagnosis technique to detect the cracking and de-bonding of the permanently embedded lead zirconate titanate (PZT). Cracks are reported as key in [5], which use the first-order reliability method (FORM + Fracture) to alleviate the computational cost of probabilistic defect-propagation analysis [6; 7; 8; 9; 10], which is commonly used to detect cracks using a special hardware device. Some algorithms for crack detection are based on deep learning. Recently, deep learning has achieved great improvements in various visual tasks such as object classification [11; 12; 13], and object detection [14; 15; 16]. Deep learning fundamentally changes the ways to tackle some traditionally hard or intractable visual tasks and has produced many successful applications. Therefore, the application of deep learning in crack detection has emerged. Recent studies have designed deep learning-based crack recognition methods: [17] proposed a 5-layers Convolutional Neural Network (CNN) to detect cracks, while [18] evaluated five CNN architectures for corrosion detection from input images, in all of which large input images were cropped into small images of a fixed size. The CNNs were applied to classify whether cracks or corrosion were contained in each small input image. [PERSON]. [19] proposed an aircraft engine borescope crack detection and segmentation system based on deep learning. [PERSON]. [20] proposed an algorithm which learned hierarchical convolutional features for crack detection, which was actually an edge detection method. Although the crack features were extracted well, their detection scenarios are very simple, so the method could not be applied to the complex scene detection of aircraft structural cracks. All of the crack detection methods mentioned above did not meet the needs of assistant technicians in order to complete rapid and accurate crack detection in aircraft structures. First, because the crack detection of aircraft structure is typically conducted in an outdoor environment, the detection equipment must be portable.