# Aircraft Target Interpretation Based on SAR Images
## 6 Conclusions  
For the accurate detection and recognition of aircraft targets in high-resolution remote sensing SAR images, an improved YOLOv5s is proposed in this paper and is of great significance. An MRFCA network is proposed for improving the detection ability for multi-scale targets, the weights of different receptive fields are dynamically adjusted by adaptive learning, reasonably allocating the proportion of feature information of multi-scale. This paper proposed the 4 DDH network, as the four detection branches can improve the detection ability for small targets, and the decoupled detection head can effectively avoid the conflict of different feature information concerned in detection and recognition tasks, strengthening the capabilities of detection and recognition. This paper integrated the multi-method of data augmentation, which can enhance the diversity of datasets and generalization of the model, and improve the training speed of the network. This paper used the K-mean++ method, which can improve the network convergence speed and detection accuracy. Experiments showed that the improved YOLOv5s significantly improves the performance of SAR image interpretation, especially for small aircraft targets.  
In the case of extremely small targets and complex backgrounds, missing detections and false detections still exist. In addition, the detection speed and model size of the improved YOLOv5s algorithm need to be optimized. To further improve the efficiency of the algorithms, we will try to integrate the following improvements into existing algorithms.  
Figure 10: Distribution of the bounding boxes of the aircraft targets in our datasets.  
Firstly, we should attempt to use the lightweight network to reduce model parameters. Secondly, we should consider to incorporate the attention mechanism to improve the accuracy and efficiency of SAR image interpretation. Thirdly, there are only 2000 SAR images in our datasets, so we should consider how to improve the model generalization. Finally, we should consider how to fuse SAR and optical images to achieve information complementarity and improve detection accuracy.  
Conceptualization, W.H. and X.W.; methodology, Y.L. and X.W.; software, X.W.; validation, D.H.; resources, Y.L. and P.X.; data curation, W.H.; writing--original draft preparation, X.W.; writing--review and editing, W.H. and Y.L.; supervision, W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the key research and development projects of the Ministry of Science and Technology of China (Grant No. 2022 YFC2203901), the Science and Technology Development Plan Project of Jilin Province, China (Grand No. 20230201099 GX), and the Department of Education Science and Technology Research Project of Jilin Province, China (Grand No. JJKH20220053 KJ).  
Not applicable.  
The dataset used in this research can be obtained from the 2021 Gaofen challenge on Automated High-Resolution Earth Observation Image Interpretation. Available online: [[http://gaofen-challenge.com](http://gaofen-challenge.com)]([http://gaofen-challenge.com](http://gaofen-challenge.com)) (accessed on 1 October 2021).  
The authors would like to thank the anonymous reviewers.  
The authors declare no conflict of interest.