# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 3 Experimental Results and Analysis
### Comparison with Other Algorithms  
In this study, DCN-YOLO was compared with other state-of-the-art remote sensing image target detection algorithms on the DOTAv1.0 and DIOR datasets, as shown in Tables 2 and 3. In comparison to the single-stage object detection algorithms YOLOv3, YOLOv3-Spp, YOLOv4-Csp, YOLOR-Csp, YOLOv5l(7.0), YOLOv5-Bifpn(7.0), YOLOv5-Transformer(7.0), YOLOv6l, and YOLOv7, the proposed method in this paper has shown significant improvements in [EMAIL_ADDRESS] values. Specifically, the [EMAIL_ADDRESS] values have increased by 4%, 3.1%, 3.1%, 3.4%, 3.5%, 5.3%, 1.6%, 3.4%, and 3.4%, respectively. The proposed model demonstrated superior performance in several performance metrics and reduced computational effort, thus verifying its effectiveness. On the DOTAv1.0 dataset, the proposed method achieved an average precision of 63.4% at IoU threshold 0.5, AP\({}_{M}^{val}\) of 36.4%, AP\({}_{L}^{val}\) of 51.2%, and notably AP\({}_{S}^{val}\) of 20.1%, indicating a significant improvement in small target detection performance compared to other indices. Similarly, on the DIOR dataset, at 640 resolution, the proposed method achieved an average precision of 90.1%, with AP\({}_{S}^{val}\), AP\({}_{M}^{val}\), AP\({}_{L}^{val}\) of 29.0%, 57.4%, and 79.6%, respectively. The results indicate a significant improvement in small target detection performance compared to other methods. Overall, the experimental results demonstrate the superior detection accuracy of the proposed method, especially for small targets. Figure 6 presents some detection results of the proposed methods, YOLOv7 and YOLOv6l, on the DOTAv1.0 dataset. The differences in the detection results are highlighted with red boxes. As shown in the four examples, Figure 6 the proposed method performs significantly better than the other methods in detecting small and dense targets such as swimming pools, airplanes, car \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Params** & **FLOPs(G)** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** & **AP\({}_{S}^{val}\)** & **AP\({}_{M}^{val}\)** & **AP\({}_{L}^{val}\)** & **Year** \\ \hline YOLOv3 [32] & 61.6 & 154.8 & 59.4\% & 38.7\% & 14.0\% & 34.2\% & 48.0\% & 2018 \\ YOLOv3-Spp & 62.6 & 156.6 & 60.3\% & 39.0\% & 15.6\% & 34.3\% & 44.1\% & 2018 \\ YOLOv4-Csp [33] & 52.5 & 119.1 & 60.3\% & 39.6\% & 14.6\% & 34.2\% & 46.5\% & 2020 \\ YOLOR-Csp [34] & 46.2 & 107.9 & 60.0\% & 39.6\% & 16.6\% & 34.1\% & 50.3\% & 2021 \\ YOLOv5l(7.0) & 46.4 & 108.7 & 59.9\% & 40.0\% & 14.5\% & 33.8\% & 50.9\% & 2021 \\ YOLOv5-Bifpn(7.0) & 7.0 & 15.7 & 58.1\% & 36.4\% & 12.8\% & 31.4\% & 44.1\% & 2021 \\ YOLOv5s- & 52.5 & 119.1 & 61.8\% & 40.4\% & 15.1\% & 34.7\% & 50.8\% & 2021 \\ Transformer(7.0) & 59.6 & 150.5 & 60.0\% & 40.1\% & 15.8\% & 35.8\% & 49.6\% & 2022 \\ YOLOv7 [36] & 36.5 & 103.4 & 60.0\% & 40.0\% & 15.9\% & 34.4\% & 48.2\% & 2022 \\ DCN-YOLO & 38.1 & 98.4 & 63.4\% & 41.9\% & 20.1\% & 36.4\% & 51.2\% & ours \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison results of the DOTAv1.0 dataset.