# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
###### Abstract  
Synthetic aperture radar (SAR) is an active ground-surveillance radar system, which can observe targets regardless of time and weather. Passenger aircrafts are important targets for SAR, as it is of great importance for accurately recognizing the type of aircraft. SAR can provide dynamic monitoring of aircraft flights in civil aviation, which is helpful for the efficient management of airports. Due to the unique imaging characteristics of SAR, traditional target-detection algorithms have poor generalization ability, low detection accuracy, and a cumbersome recognition process. Target detection in high-resolution SAR images based on deep-learning methods is currently a major research hotspot. You Only Look Once v5 (YOLOv5) has the problems of missed detection and false alarms. In this study, we propose an improved version of YOLOv5. A multiscale feature adaptive fusion module is proposed to adaptively assign different weights to each scale of the feature layers, which can extract richer semantic and textural information. The SIOU loss function is proposed to replace the original CIOU loss function to speed up the convergence of the algorithm. The improved Ghost structure is proposed to optimize the YOLOv5 network to decrease the parameters of the model and the amount of computation. A coordinate attention (CA) module is incorporated into the backbone section to help extract useful information. The experimental results demonstrate that the improved YOLOv5 performs better in terms of detection without affecting calculation speed. The mean average precision (_mAP_) value of the improved YOLOv5 increased by 5.8% compared with the original YOLOv5. synthetic aperture radar; passenger aircraft target; YOLOv5; multiscale feature adaptive fusion (MFAF); Ghost; coordinate attention (CA); SIOU loss Article  
SAR Image Aircraft Target Recognition Based on  
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for target detection in high-resolution SAR imagery, which took both the structural information and the statistical distribution into account to achieve a better effect with respect to SAR aircraft detection. [PERSON] et al. [3] proposed an optimized target-attitude-estimation method, which used a multilayer neural network to obtain the prior shape information to reconstruct the SAR image. This method was designed to solve the problem of the rotation of the aircraft target and was capable of highly accurate detection. [PERSON] et al. [4] proposed a hierarchical fusion method of complementary features for SAR images, which integrated the features of principal component analysis (PCA), attributed scattering centers (ASCs), and the target's outline. An experiment proved that it was effective and robust. These traditional methods still have many shortcomings. The most severe problems are low accuracy, high missed detection rates or false alarm rates, and poor robustness of the algorithm [5]. With improvements in computing power and the emergence of advanced algorithms, artificial intelligence has achieved excellent performance in the field of computer vision. It is mainly used for the recognition, detection, segmentation, and reconstruction of the target in the image. In this study, our SAR-image-interpretation work aimed to recognize the specific type of aircraft in the SAR images. A target-recognition method based on a convolutional neural network (CNN) can solve the complicated problem of manual calculation and artificial feature extraction. With the continuous development and maturity of deep-learning theory, target-detection algorithms have achieved great results. The current mainstream detection algorithms are mainly divided into two categories: one-stage and two-stage detection. Two-stage detection algorithms include the region convolutional neural network (R-CNN) algorithm [6], Fast R-CNN [7], Faster R-CNN [8], and Mask R-CNN [9].