## 1 Introduction
However, the methods mentioned above require specialized professional and complex equipment for crack detection, which is inconvenient in aircraft inspection. Second, aircraft structures, especially the internal background of the engine, are very complex, and the performance of the algorithm in complex environments is very demanding. However, the background of the crack images used in the methods mentioned above is very clean, as is shown in Figure 1. Most of the cracks in other works are similar to those shown in the last two figures. Third, crack detection in aircraft structures needs to be completed accurately in a limited time. So, the offline detection speed of the algorithm needs to be close to real-time and guarantee high accuracy as well. However, when the efficiency of the above methods was improved, the accuracy decreased significantly. These two indicators cannot be taken into account simultaneously by the above methods. To solve the problems mentioned above, we propose an aircraft structure crack detection algorithm: YOLOv3-Lite. This method is a deep learning-based framework, which can accurately and efficiently detect crack damage on an aircraft structure. Compared with the previous crack detection method, YOLOv3-Lite shows that it can reach state-of-the-art performance. It can run on a mobile device due to its light-weight characteristics. On the premise of guaranteeing a certain accuracy rate, the speed has been greatly increased. In addition, the speed improvement of YOLOv3-Lite refers to the shortening of offline detection time. The algorithm pipeline can be described by three parts. Firstly, set is divided into the training set, the validation set, and the test set. The data is processed in a form that can be received by YOLOv3-Lite. Secondly, the YOLOv3-Lite architecture is built, which adopt depthwise separable convolution, feature pyramid, and YOLOv3 [21] methods. Additionally, transfer learning is introduced to reduce the required amount of data and yield high accuracy. We evaluate the model by comparing with baselineperformance and achieve comparable AP scores and competitive efficiency as well. Finally, the model is applied to test data. The pipeline is shown in Figure 2.  
The special contribution of this paper are as follows:  
1. The proposed algorithm is a lightweight network with fewer parameters, such that it can be migrated to mobile devices. 2. It is a novel aircraft structure crack detection network which can detect the different parts of an aircraft and can detect different types of an aircraft, which means it has good generalization performance. 3. The crack detection network is combined with depthwise separable convolution and feature pyramids so it is fast and accurate. The rest of the paper is organized as follows: Section 2 explains the YOLOv3-Lite algorithm. Section 3 shows some experiments using YOLOv3-Lite and discusses the experimental results. Finally, in Section 4, some future works are presented and the paper is concluded. Figure 1: A comparison of aircraft structure cracks and crack images used in other works. (**a**) crack in fuselage, (**b**) crack in engine, (**c**) cracks in the pavement, (**d**) cracks in concrete. Figure 2: The overall architecture of the proposed YOLOv3-Lite algorithm.