# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 3 Experimental Results and Analysis
### Ablation Experiments  
To comprehensively analyze the effectiveness of various improvement strategies, we randomly selected 10,000 images from the cropped DOTAv1 dataset and divided them into training, validation, and test sets in a 7:1:2 ratio. Ablation experiments were conducted to compare and analyze the impacts of different improvement strategies on the model's detection performance. The experiments were based on the original YOLOv7, and different improvements were added to it separately, with the original algorithm serving as the control group. The specific experimental results are presented in Table 4.  
Through experimental comparison, improvement 1 adds a detection head to the basic model and utilizes the K-means++ algorithm to cluster anchor boxes, resulting in a 1.2% increase in [EMAIL_ADDRESS]:0.95 compared to the initial model, with the small target detection index AP\({}_{S}^{val}\) increasing by 3.3%. The anchor box matching method for remote sensing images was also redesigned to better suit the four-scale feature layer of the network framework, greatly improving the model's detection ability for small targets and effectively reducing the occurrence of missed detections and false positives. In the second improvement, while the detection performance of the basic model remained unchanged, the computational time complexity (FLOPs) of the improved model was reduced by 11.8%, making it more convenient for deployment on embedded mobile terminals and allowing users to choose a lower-cost processor by sacrificing a small amount of detection performance. An improvement of 3%, utilizing SFPN, had a positive impact on object detection accuracy in remote sensing images, especially for the large target index AP\({}_{L}^{val}\), which increased by 2.9%. The module introduces the SimAM attention mechanism at the lateral junction of the FPN to improve the fusion ability of deep semantic information and shallow texture information and further focus on the target feature information. In the fourth improvement, the NGWD loss function was introduced as the regression loss on the basic model, resulting in a 1.5% increase in [EMAIL_ADDRESS] and greatly improving the network's detection accuracy.  
To further demonstrate the effectiveness of the proposed improvements, we visualized the feature maps extracted from the network, with red indicating that the model attends more to the target region and blue indicating vice versa. Five sets of images were randomly selected for feature map visualization, as shown in Figure 7. The feature maps illustrate that the improved model pays more attention to the target regions than the original model when processing these images, with red regions more focused on the target regions and blue regions more distributed in the background. This indicates that the improved model can better capture the target features in the images, thereby improving the model's accuracy and performance. Additionally, the feature maps of different images show that the target features in different images are distinct, indicating that the improved model can better adapt to various types of image data.  
\begin{table}
\begin{tabular}{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\({}_{L}^{val}\)** \\ \hline Base & 36.5M & 103.4 & 61.5\% & 40.4\% & 15.9\% & 34.4\% & 48.2\% \\ +Head & 37.1M & 117.3 & 61.5\% & 41.2\% & 19.2\% & 34.6\% & 47.1\% \\ +DCNv2 & 36.9M & 91.2 & 61.1\% & 39.7\% & 16.0\% & 33.9\% & 47.6\% \\ +SFPN & 36.5M & 103.4 & 62.1\% & 40.8\% & 15.8\% & 34.6\% & 52.2\% \\ +NGWD & 36.6M & 103.4 & 63.0\% & 40.8\% & 16.8\% & 34.5\% & 51.1\% \\ Our & 38.1M & 98.4 & 63.5\% & 41.9\% & 20.1\% & 36.4\% & 51.2\% \\ improvement & +4.4\% & \(-\)4.8\% & +2 & +1.5 & +4.2 & +2.0 & +3.0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection performance of different improvement strategies on the DOTA dataset.