# Aircraft Target Interpretation Based on SAR Images
###### Abstract  
The focus of the current research is how to effectively identify aircraft targets in SAR images. There are only 2000 SAR images and 6556 aircraft instances in our dataset. SAR images have complex backgrounds, and the sizes of aircraft targets are multi-scale. How to improve the detection accuracy of aircraft targets is the research topic of this paper, especially for small target detection. We proposed four improved methods based on YOLOv5s. Firstly, this paper proposed the structure of the multi-scale receptive field and channel attention fusion. It is applied at the shallow layer of the backbone of YOLOv5s. It can adjust the weights of the multi-scale receptive field during the training process to enhance the extraction ability of feature information. Secondly, we proposed four decoupled detection heads to replace the original part in YOLOv5s. It can improve the efficiency and accuracy of SAR image interpretation for small targets. Thirdly, in the case of the limited amount of SAR images, this paper proposed multiple data-augmentation methods, which can enhance the diversity and generalization of the network. Finally, this paper proposed the K-means++ to replace the original K-means to improve the network convergence speed and detection accuracy. Experiments demonstrate that the improved YOLOv5s can enhance the accuracy of SAR image interpretation by 9.3%, and the accuracy of small targets is improved more obviously, reaching 13.1%. YOLOv5s; attention mechanism; decoupled detection head; K-means++; aircraft detection; SAR image +
Footnote †: journal:constant false alarm rate (SOCA-CFAR), greatest of cell average constant false alarm rate (GOCA-CFAR), ordered statistic constant false alarm rate (OS-CFAR), and variability index constant false alarm rate (VI-CFAR). Secondly, the detector can take advantage of the characteristics of targets to eliminate false alarms, and the common characteristics include contour, size, texture, scatter center, etc. [3; 4; 5; 6]. Finally, the recognizer can classify the object of targets, and the universal algorithm contains template-based methods, mathematical model-based methods, and machine learning-based methods [7; 8]. Based on these, scholars have proposed improved methods [9; 10; 11]. Although these methods can shorten detection time and improve detection precision to a certain extent, the traditional interpretation methods can not satisfy the needs of detection speed and accuracy, and their generalization ability is poor, which is greatly affected by the datasets. In recent years, driven by artificial intelligence technology, deep learning algorithms have been applied to the following fields: facial recognition, natural language recognition, vehicle detection, and so on. All of these applications have achieved fruitful results. The deep learning algorithms consist of complex network structures, which can extract deeply abstract semantic information. The detection and recognition performance based on deep learning algorithms has already exceeded that of the traditional interpretation method. More and more excellent algorithms have been proposed. The existing deep learning algorithm for detection and recognition divides into one-stage and two-stage detection. The two-stage detection includes regions with convolutional neural network (R-CNN) [12], Fast R-CNN [13], Faster R-CNN [14], and Mask R-CNN [15]. They used the region proposal network (RPN) to generate candidate boxes, then perform regression and classification on the candidate boxes. The one-stage detector includes SSD [16], Retina-Net [17], and YOLO [18; 19; 20]. The algorithms of the YOLO series are generally faster than others, and it has a good performance for small objects, so it has been widely applied in computer vision and pattern recognition recently. Many scholars have conducted research based on the YOLOv5 version, and proposed many improved methods. [PERSON] et al.