# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 2 Related Work  
In 2016, [PERSON] et al. proposed the YOLO algorithm, which utilized the idea of regression to complete the steps of target detection and recognition in one stage. Compared with other deep-learning algorithms, YOLO has the advantage of a rapid detection, which has high value for application in many fields. At present, YOLO has been developed to the fifth generation. Through continuous improvement, its performance and network structure have been optimized. In this study, we selected YOLOv5 as the baseline.  
YOLOv5 is divided into four versions according to the size of the model: YOLOv5x, YOLOv5l, YOLOv5m, and YOLOv5s. YOLOv5s is the smallest, lightweight version and has the fastest detection speed. YOLOv5x is the largest version and has high feature extraction ability, fusion ability, and detection accuracy. The network structure of YOLOv5 is generally divided into four sections: the input, backbone, neck, and head.  
* Input: The images in the dataset are fed to the input section for training and validation. The input stage adopts the mosaic data augmentation method [24], which uses four images for random scaling, random clipping, and random layout, and then splices them into one image as training data. It can effectively enrich the dataset and improve the usage efficiency of a GPU. The input stage adopts the adaptive image scaling method to scale the original image to the standard size and sends it into the network for training. It can reduce the amount of calculation and improve the speed of detection. The input stage also adopts the adaptive anchor box method, which uses a K-means clustering algorithm for different datasets. It can calculate the best initial value of an anchor box for different training sets. It can also accelerate the convergence of the algorithm.
* Backbone: This is used to extract feature maps of different receptive fields. The backbone is mainly composed of CBS, C3, and SPPF. CBS is a composite convolution module and is a basic component of many important models. CBS is composed of Conv2d, BN, and SiLU, which are used to extract the features. C3 is composed of several bottleneck residue modules, which improve the learning ability of the model, lighten the model, ensure the recognition accuracy of the network, and reduce the model's cost and memory space. SPPF transmits the input serially through three max-pooling layers of the 5 \(\times\) 5 convolution core and concatenates the output of each max-pooling layer together. It is used to extract the features and enhance the receptive field of the network. SPPF has the same function as SPP, but SPPF is more efficient and faster.
* Neck: This is used to fuse the feature maps of different receptive fields. The neck combines the structure of feature pyramid networks (FPNs) [25] and the path aggregation networks (PANs) [26]. An FPN conveys semantic information from top to bottom, while a PAN conveys positioning information from bottom to top. The combination of the two improves the feature fusion capability of the network by aggregating the parameters of different receptive fields from different backbone layers. It can greatly improve the recognition accuracy for multiscale targets and the performance in terms of detecting dense targets.
* Head: YOLOv5 has three detection heads, which are used to predict the passenger aircraft targets at different scales in SAR images. The head section mainly includes the bounding box loss function and non-maximal suppression (NMS) [27]. CIOU is used as the loss function in YOLOv5 and accounts for the geometric factors of the distance from the center, the overlapping area, and the aspect ratio. It can make the algorithm converge quickly. In the prediction stage, the weighted NMS operation is adopted to obtain the optimal target box from the numerous target boxes that appear, which can enhance the performance of the model in terms of recognition.