# RS-YOLOX: A High-Precision Detector for Object Detection in Satellite Remote Sensing Images
## 4 Experiments
### Evaluation Model  
We added the above improved methods to the original YOLOX model in turn and tested them with the cut and amplified DOTA-v1.5 dataset. The results obtained are displayed in Table 5.  
Figure 10: The detection results of the YOLOX model combined with the SAHI framework for the same high-resolution image. Figure 9: The detection result of putting a large-size image directly into the YOLOX mode. After we improved the YOLOX model, the test results on the DOTA dataset were 4.92% higher than the mAP of the original YOLOX model, but there was a slight increase in the number of parameters and calculations. Finally, we fused the improved YOLOX and SAHI frameworks so that our model could directly detect large-size remote sensing images. By improving YOLOX using all the above methods, we produced our new model: RS-YOLOX. It can be seen from the experimental results that, compared with YOLOX, RS-YOLOX greatly improved the detection performance for aerial remote sensing images with extremely high resolution. Figure 11 shows a comparison of object detection results of YOLOX and RS-YOLOX on the DOTA-v1.5 dataset. Our model also achieved good performance on two other datasets. Figure 12 displays its detection results on TGRS-HRSD and RSOD datasets. The official website of TGRS-HRSD provides the detection results of several advanced models. Table 6 presents a comparison between our model and these models. Figure 11: Comparison of detection results of YOLOX and RS-YOLOX on the DOTA-v1.5 dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **mAP\({}_{\text{50-95}}\) (\%)** & **P (M)** & **Latency (ms)** & **FPS** \\ \hline YOLOX & 73.57 & 8.94 & 13.66 & 73.2 \\ +ECA & 75.58 (+2.01) & 8.94 & 14.46 & 69.2 \\ +ASFF & 77.16 (+1.58) & 14.38 & 17.96 & 55.7 \\ +VFL & 78.49 (+1.33) & 14.38 & 17.67 & 56.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The test results of adding each module to YOLOX in turn on DOTA-v1.5. On the TGRS-HRRSD dataset, our model has the highest mAP value and our model has a higher detection accuracy for each category than the models provided by the official website. Finally, we tested the current state-of-the-art remote sensing image detectors with these three datasets, and the results obtained are displayed in Table 7. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Category** & **YOLOv2/\%** & **Fast R-CNN/\%** & \begin{tabular}{c} **Fast R-CNN +** \\ **GACL-Net/\%** \\ \end{tabular} & \begin{tabular}{c} **Faster** \\ **R-CNN/\%** \\ \end{tabular} & \
\begin{tabular}{c} **Faster** \\ **+ GACL-Net/\%** \\ \end{tabular} &
\begin{tabular}{c} **Faster** \\ **(Ours)/\%** \\ \end{tabular} \\ \hline Airplane & 84.6 & 83.3 & 85.1 & 90.8 & 90.8 & **90.9** \\ Baseball Diamond & 62.2 & 83.6 & 82.6 & 86.9 & 87.2 & **88.6** \\ Basketball Court & 41.3 & 36.7 & 42.1 & 47.9 & 49.7 & **78.2** \\ Bridge & 79.0 & 75.1 & 76.7 & 85.5 & 85.6 & **90.4** \\ Crossroad & 43.4 & 67.1 & 68.7 & 88.6 & 88.2 & **90.1** \\ Ground Track Field & 94.4 & 90.0 & 89.6 & 90.6 & 90.7 & **90.8** \\ Harbor & 74.4 & 76.0 & 78.4 & 89.4 & 89.7 & **89.9** \\ Parking Lot & 45.8 & 37.5 & 39.5 & 63.3 & 65.3 & **75.0** \\ Ship & 78.5 & 75.0 & 74.3 & 88.5 & 88.5 & **90.1** \\ Storage Tank & 72.4 & 79.8 & 80.4 & 88.7 & 89.2 & **90.3** \\ T Junction & 46.8 & 39.2 & 38.8 & 75.1 & 75.0 & **86.5** \\ Tennis Court & 67.6 & 75.0 & 77.0 & 80.7 & 80.0 & **90.4** \\ Vehicle & 65.1 & 46.1 & 50.7 & 84.0 & 86.9 & **90.8** \\ mAP\({}_{50}\) & 65.8 & 66.5 & 68.0 & 81.5 & 82.1 & **88.0** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of our model with the models on the HRRSD official website. Figure 12: Detection results of our model on TGRS-HRRSD and RSOD. (**a**) From left to right, the photographs show ground track fields, tennis courts, baseball diamonds, and basketball courts. (**b**) From left to right, the photographs show aircraft, oil tanks, an overpass, and a playground.