# SAR Image Aircraft Target Recognition Based on Improved YOLOv5
## 5 Conclusions  
We proposed an improved YOLOv5 structure to recognize aircraft targets in SAR images. To meet detection speed requirements, we chose YOLOv5s as the baseline. We designed the MFAF module, which was applied as the head section of YOLOv5s. The MFAF module could better fuse features with different scales, and it effectively improved the precision of detection. However, it increased the size of the model and reduced the speed of detection. To solve these problems, we designed the Ghost-Conv and SE-Ghost-C3 lightweight network structure and adopted the SIOU loss function. A large number of experiments proved that these methods were effective. To further improve the ability to extract features, we adopted the CA mechanism, which was embedded into the backbone of YOLOv5s. It also improved the precision of detection. Compared with original YOLOv5s,  
Figure 9: Comparison of the results for the original YOLOv5s and the improved YOLOv5s. (**a**) Bounding boxes of the SAR images in the dataset. (**b**) Results for the original YOLOv5s. (**c**) Results for the improved YOLOv5s.  
our algorithm effectively improved the precision of detection without affecting the model's size and the speed of detection. Compared with current state-of-the-art algorithms, our improved algorithm also achieved the best performance in terms of detection.  
SAR images contain a large amount of speckled noise. Moreover, the size of the aircraft targets in SAR images is also on multiple scales. These factors interfere with the recognition of aircraft targets. To further improve the precision of detection, future research should mainly focus on the following aspects. Firstly, we should consider how to preprocess SAR images to reduce the impact of speckled noise. Secondly, we should consider how to optimize the structure of the neck section to better fuse the multiscale features. Thirdly, there is currently only one publicly available dataset for recognizing aircraft targets in SAR images, so we should consider how to improve the generalizability of the improved model we designed here in different scenarios.  
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 subject of the Ministry of Science and Technology of China (Grant No. 2022 YFC2203901), Science and Technology Development Plan Project of Jilin Province, China (Grand No. 20230201099 GX), 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.