## 5 Conclusions  
In this paper, we optimized YOLOX and proposed an anchor-free object detection network model for detecting objects in remotely sensed images, called DCE_YOLOX. In order to solve the problems of low resolution, complex background, and scale change of the detection object, it uses depth separable deconvolution for upsampling, which can effectively recover the lost feature information while expanding the feature map. Moreover, it combines dilated convolution and CoTNet for feature extraction, makes full use of local context information, and expands the receptive field. It also adds EcaNet to the enhanced feature extraction network of the original network model, uses the feature information through the effective channel attention module, makes the model pay more attention to information-rich features, and improves the detection ability of the network model to small objects. Then, according to the high image resolution characteristics of remote sensing images, the network input size is optimized to improve the detection performance of the model for small objects. Furthermore, CSL was used to calculate the angle loss and realize the rotation object detection of remote sensing images. Finally, experiments were carried out on the multiscale, high image resolution DOTA dataset and the HRSC2016 dataset. Our method pays much more attention to small objects, especially small objects in images. Although the detection speed has decreased, the detection ability exceeds other commonly used algorithms. In the future, we will further optimize the feature representation of the model, enhance the generalization ability of the model, and study different annotation methods, while ensuring faster detection speeds.  
Conceptualization, H.Z. and W.G.; methodology, W.G.; software, W.G.; validation, W.G. and Q.Z.; formal analysis, W.G.; data curation, W.G.; writing--original draft preparation, W.G.; writing--review and editing, H.Z.; visualization, Q.Z.; supervision, H.Z.; project administration, Q.Z. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**DS-DConv** & **DCoTNet** & **EcaNet** & **O-N-I-S** & **CSL** & **Precision** & **Recall** & **mAP** \\ \hline  & & & & & \(\bigvee\) & 0.74904 & 0.68846 & 0.72001 \\  & & & & \(\bigvee\) & 0.74896 & 0.70741 & 0.73304 \\  & & & \(\bigvee\) & \(\bigvee\) & 0.75729 & 0.70814 & 0.73422 \\  & & \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & 0.76333 & 0.70947 & 0.76142 \\ \(\bigvee\) & & \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & 0.77661 & 0.72814 & 0.76413 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Ablation study on DOTA dataset.  
**Funding:** This research was funded by Anhui University of Science and Technology, Huaii'nan, China, National Natural Science Foundation of China 61703005, and Anhui Province Key R&D Program of International Science and Technology Cooperation Special Project 202004b11020029.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors thank Anhui University of Science and Technology for its support. Secondly, they thank ITVR-AUST laboratory for its support.  
**Conflicts of Interest:** The authors declare no conflict of interest.