# YOLO-SE: Improved YOLOv8 for Remote Sensing Object Detection and Recognition
## 4 Experimental and Results Analysis
### Results Analysis  
#### 4.2.1 Experimental Results  
The AP50 for various categories in the SIMD dataset reached 86.5%. Table 2 presents the detection results for all categories using the YOLO-SE algorithm on the SIMD dataset. To further validate the effectiveness of the algorithm, we conducted experiments on the NWPU VHR-10 dataset. NWPU VHR-10 is a publicly available geospatial object detection dataset comprising a total of 800 remote sensing (RS) images. We split the dataset into 80% for training and 20% for testing. Table 3 presents the detection results on the NWPU VHR-10 dataset; YOLO-SE achieved an accuracy of 94.9%.  
Figure 9 displays some visual results of the proposed method on the SIMD dataset. It is evident from Figure 6 that the algorithm introduced in this paper effectively addresses the challenges of multi-scale objects and noise in complex environments. Additionally, it demonstrates good performance in detecting small objects.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Categories** & **P/\%** & **R/\%** & **AP50/\%** & **mAP/\%** \\ \hline car & 85.7 & 91.3 & 94.1 & 75.0 \\ truck & 80.4 & 79.6 & 86.0 & 71.0 \\ van & 79.7 & 80.8 & 85.8 & 69.6 \\ long vehicle & 80.4 & 82.1 & 84.8 & 68.3 \\ bus & 87.0 & 88.3 & 93.8 & 79.6 \\ airliner & 93.3 & 96.6 & 98.9 & 91.3 \\ propeller & 92.0 & 93.9 & 95.1 & 82.9 \\ trainer & 91.3 & 99.2 & 98.2 & 85.4 \\ chartered & 87.1 & 97.2 & 95.2 & 85.7 \\ fighter & 79.3 & 100.0 & 97.1 & 87.0 \\ other & 62.4 & 42.7 & 44.7 & 34.5 \\ stair truck & 68.9 & 57.8 & 66.5 & 46.1 \\ pushback truck & 88.2 & 50.8 & 68.0 & 49.0 \\ helicopter & 82.0 & 70.4 & 90.8 & 53.3 \\ boat & 96.2 & 98.1 & 98.5 & 81.1 \\ all & 83.6 & 81.9 & 86.5 & 70.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detection results on SIMD dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Class** & **P/\%** & **R/\%** & **AP50/\%** & **mAP/\%** \\ \hline airplane & 87.8 & 94 & 97.2 & 88.7 \\ ship & 86.2 & 100 & 99.5 & 93.2 \\ storage tank & 99.2 & 100 & 99.5 & 84.8 \\ baseball diamond & 98.1 & 95.9 & 97.9 & 82.6 \\ tennis court & 94.5 & 93.3 & 97.6 & 81.5 \\ basketball court & 100 & 83.2 & 95.3 & 58.1 \\ ground track field & 97.2 & 76.9 & 92.5 & 60.1 \\ harbor & 88.5 & 86.7 & 93.6 & 69.5 \\ bridge & 90.3 & 100 & 98 & 86.3 \\ vehicle & 64.6 & 80 & 77.5 & 67.3 \\ all & 90.6 & 91 & 94.9 & 77.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Detection results on NWPU VHR-10 dataset.  
Figure 10 displays Precision-Recall (PR) curves for each class in the SIMD dataset, illustrating the average precision for each class. From the graph, we can observe that categories like 'airliner,' 'propeller,' 'trainer,' 'chartered,' 'fighter,' and 'boat' perform remarkably well, with average precisions all exceeding 0.95. In contrast, the 'other' category has an average precision of only 0.447. This could be attributed to the 'other' category being diverse, with no unified features for the network to learn.  
Figure 11 shows the progression of various metrics during training and validation, including box loss, object loss, class loss, and metrics after each epoch, such as accuracy and recall.  
Figure 12 presents the confusion matrix for our model. It is evident that 'other,''stair truck,' and 'pushback truck' are sometimes not detected and are considered as background. Moreover,'stair truck' and 'pushback truck' have a similar appearance, making it challenging to distinguish them in remote sensing images.  
Figure 9: Visual results on the SIMD dataset.  
Figure 11: Network convergence on the SIMD dataset.  
Figure 10: The P-R curve during training on SIMD dataset.