# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 5 Conclusions  
To address the challenges of multi-scale and small-object detection in remote sensing image detection, this paper introduced the YOLO-SE network based on YOLOv8. First, we successfully introduced the SEF module, a lightweight design that significantly improves network parameters and inference speed. The SEF module effectively handles multi-scale features, providing a solid foundation for the model's performance. Second, by introducing the SPPFE module, we not only improved the efficiency of feature extraction but also successfully introduced the EMA attention mechanism. The multi-scale convolution operations of this module help capture different scale information, thus enhancing the model's accuracy. At the same time, we added an additional prediction head specifically designed for detecting tiny objects. When combined with the other three prediction heads, this allowed us to capture relevant information about small targets more effectively. We also replaced the original detection head with a transformer prediction head (TPH) to capture more global and contextual information. Finally, to improve the training process, we introduced the Wise-IoU bounding box loss function. The use of this loss function helps reduce the negative impact of low-quality instances during training, improving the model's stability and robustness. The experimental results demonstrated that YOLO-SE achieved significant performance improvement on the optical remote sensing dataset SIMD, with a mAP value of 86.5%--a 2.1% increase compared to YOLOv8. This research demonstrated that the introduction of multi-scale convolutions, attention mechanisms, and transformer prediction heads can achieve higher performance in the field of remote sensing image object detection while maintaining a certain level of efficiency. This provides powerful tools and methods for remote sensing image analysis.  
In our upcoming efforts, we will aim to further optimize the network model by prioritizing a balance between reduced complexity and improved detection accuracy. We intend to expand the application of the proposed network structure modifications to various object detection algorithms. Moreover, we will investigate alternative strategies for feature reuse and thoroughly address deployment and application challenges associated with the algorithm discussed in this paper.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **SEF** & **SPPFE** & **WIoU** & **TPH** & **AP50(\%)** \\ \hline Exp 1 & & & & & 84.4 \\ Exp 2 & \(\surd\) & & & & 85.3 \\ Exp 3 & \(\surd\) & \(\surd\) & & & 85.5 \\ Exp 4 & \(\surd\) & \(\surd\) & \(\surd\) & & 85.9 \\ Exp 5 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 86.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of ablation test, “\(\surd\)” represents the selection of the corresponding method.  
**Author Contributions:** Conceptualization, T.W.; methodology, T.W.; software, T.W.; validation, T.W.; formal analysis, T.W.; investigation, T.W.; resources, T.W.; data curation, T.W.; writing--original draft preparation, Y.D.; writing--review and editing, Y.D.; visualization, Y.D.; supervision, Y.D.; project administration, T.W.; funding acquisition, T.W. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement**: The data set used in the paper can be downloaded here: GitHubians/simd:SatelliteImageryMulti-ehiclesDataset(SIMD), [[https://github.com/ihians/simd](https://github.com/ihians/simd)]([https://github.com/ihians/simd](https://github.com/ihians/simd)) (accessed on 28 January 2021).  
**Conflicts of Interest**: The authors declare no conflict of interest.