# Aircraft Target Interpretation Based on SAR Images
###### Abstract
[21] proposed a multi-scale detection RebarNet network with an embedded attention mechanism based on YOLOv5 to solve the problem of missed and false detection in dense small-object detection. [PERSON] et al. [22] proposed integrating coordinate attention (CA) into the backbone of the deep network, enhancing the expression ability of features and the precision of detection and recognition. [PERSON] et al. [23] proposed adding one more detection branch based on the YOLOv5 structure, which can improve the efficiency of detection and recognition for small targets. [PERSON] et al. [24] proposed the feature fusion method of multi-scale adaptive, which can retain more useful feature information. It can improve the precision of detection and recognition for multi-scale targets. [PERSON] et al. [25] created a SAR-optical fusion dataset (QXS-SAROPT), which contains 20,000 pairs of SAR-optical image patches. Meanwhile, the author presented an application of SAR ship detection based on QXS-SAROPT datasets. The average precision (AP) of SAR ship detection has improved by cross-modal information from optical images. The methods used in the literature have not applied to the aircraft target interpretation of SAR images. Based on the features of SAR images, we transferred these methods to SAR image interpretation and achieved good recognition results. SAR images of aircraft usually cover multi-scale targets, which contain lots of background noise. The accuracy of detection and recognition of existing algorithms has a great development potential, especially for small aircraft targets. This paper focused on the problem of how to enhance the precision of small aircraft targets and how to suppress the background noise from scattering spots of ground-interfering objects. The task of this study was to recognize the seven categories of aircraft in our SAR image datasets (Boeing 787, A330, Boeing 737-800, A320/321, ARJ21, A220, and others). It not only needs to mark the bounding box of the aircraft, but also needs to identify the type of aircraft. Currently, there were few cases of applying deep learning algorithms to the field of SAR image aircraft target interpretation, so this paper selected YOLOv5s as the baseline. We proposed an improved model to resolve the above mentioned problems. There are four primary differences compared with the original YOLOv5s. 1. This paper proposed the multi-scale receptive field and channel attention (MRFCA) module based on SENet [26] and the inception network [27]. The MRFCA module was integrated into the backbone of YOLOv5s, which can change the adaptively receptive field for the multi-scale targets and capture more relevant information and critical features. 2. An additional detector was integrated into the YOLOv5s. All the detect heads adopt decoupled operations. The new four decoupled detection heads (4 DDH) structure can improve detectability for multi-scale targets and enhance detection precision for small targets. 3. Flip, scaling, and mosaic data augmentation methods were fused to enhance the diversity of datasets and the generalization ability of the model and prevent overfitting [28]. 4. This paper adopted the K-means++ to replace the original K-means algorithm to improve the network convergence speed and detection accuracy [29]. The experiment showed that the mean average precision (mAP) of the SAR image interpretation based on our improved YOLOv5s algorithm is higher than the original YOLOv5s algorithm and other algorithms (Faster-RCNN, Retina-Net, SSD, YOLOv3), especially for small aircraft targets. It demonstrated that our improved YOLOv5s model has powerful feature extraction and background noise suppression ability.