# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
###### Abstract
These models first need to generate candidate boxes, then perform regression and classification operations. A candidate-box-prediction network is integrated into the deep network to realize end-to-end detection. It improves detection performance but reduces detection speed. One-stage detection algorithms include the single-shot multibox detector (SSD) [10], Retina-Net [11], and YOLO [12]. These models do not utilize the intermediate candidate box prediction process and can obtain results directly from the picture. While ensuring recognition accuracy, the calculation demand of the algorithm is reduced, and the target can be detected more quickly. Deep-learning target-detection algorithms are mostly used in optical images. In recent years, scholars from China and overseas have used different CNN architectures and deep-learning algorithms to study the interpretation of SAR images. [PERSON] et al. [13] proposed a cascaded three-look network, which combined the advantages of Faster R-CNN and residual units. It decreased false alarms and improved the detection precision. [PERSON] et al. [14] proposed an improved YOLOv5 detection method using a convolutional block attention module (CBAM) [15] and a bidirectional feature pyramid network (BiFPN) [16]. It solved the problem of missed detection in multiscale targets. [PERSON] et al. [17] proposed an adaptive deformable network (ADN) to alleviate the problems of multiscale targets and attitude sensitivity. Extensive experiments have demonstrated that the average precision of aircraft target detection in SAR images is 89.34%, which is higher than that of other mainstream algorithms. However, these research methods are mainly used for target positioning and do not require recognition of the type of target. The main research aim of this study was to recognize the type and position of passenger aircrafts in SAR images. There are seven categories of passenger aircraft in our dataset, including Boeing 787, A330, Boeing 737-800, A320/321, ARJ21, A220, and others. Being able to successfully recognize these aircraft targets in a timely way would help to achieve efficient airport management. Considering the requirements of speed and accuracy of algorithm detection, in this study we selected YOLOv5 as the basic framework. YOLOv5 is a typical one-stage detection network with the advantage of a fast detection speed, and it integrates current popular and effective algorithms with highly accurate detection. However, experiments have shown that YOLOv5 still has much room for improvement. In this study, we propose an improved YOLOv5 architecture. Our contributions can be summarized as follows. 1. A multiscale feature adaptive fusion (MFAF) module is proposed to fuse feature layers with three different scales and to adaptively adjust the contribution of the shallow and deep feature layers. This is more conducive to extracting the feature information of multiscale targets. 2. We update the loss function from CIOU [18] to SIOU [19; 20], which considers the angle factor. This effectively reduces the degrees of freedom of the regression, accelerates the convergence of the algorithm, and further improves the accuracy of the regression. 3. We use the improved Ghost [21; 22] lightweight structure to optimize the network of YOLOv5. This method not only reduces the total number of network parameters and computational complexity but also improves the network's calculating speed. 4. We use a coordinate attention (CA) [23] module as the backbone section of YOLOv5, which enhances the ability to extract features and improves the accuracy of aircraft recognition in SAR images. The improved YOLOv5 was applied to the dataset of SAR images for the recognition of aircraft targets. It exhibited more precise recognition compared with the original YOLOv5. The specific organization of this article is as follows. Section 2 describes the structure and function of each section of YOLOv5. Section 3 introduces the four improvement measures proposed in this study in detail.