# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 5 Simulation Results
### Evaluation Model  
Furthermore, in order to measure the excellent detection accuracy of the KCFS-YOLOv5 object detection method, we will present the results of a series of comparative experiments in the following article. The results from Table 10 show that compared with the conventional object detection method, the KCFS-YOLOv5 method significantly improves the detection performance of tiny objects in aerial remote sensing images. The KCFS-YOLOv5 achieves the best Map detection result compared with other advanced algorithms on the NWPU VHR-10 dataset. Meanwhile, the detection results of each object category in the dataset are greatly improved compared with the conventional algorithm. The simulation results show the excellent performance of the improved YOLOv5 algorithm, and Table 10 shows the detection effect comparison of the KCFS-YOLOv5 with the conventional algorithms on the NWPU VHR-10 dataset. Our KCFS-YOLOv5 has achieved the highest detection accuracy, and some detection results are shown in Figure 13.  
In order to further measure the performance of KCFS-YOLOv5 object detection method on other datasets, we used the KCFS-YOLOv5 algorithm to detect the object on RSOD dataset and the UCAS-AOD-CAR dataset, respectively, and the results of comparative experiments on the RSOD dataset are shown in Table 11. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Category** & **SSD\%** & **Faster-RCNN/\%** & **YOLOv3\%** & **YOLOv5\%** & **CAD [49]\%** & **ATTS [50]\%** & **CANet[51]\%** & **Ours/\%** \\ \hline Airplane & 85.1 & 91.6 & 89.8 & 98.6 & 97.0 & 99.8 & 99.9 & 99.5 \\ Ship & 72.7 & 81.9 & 77.9 & 83.3 & 77.9 & 92.5 & 86.0 & 85.1 \\ Storage tank & 80.6 & 83.0 & 83.3 & 97.6 & 95.6 & 97.1 & 99.3 & 98.1 \\ Baseball diamond & 85.1 & 85.8 & 89.4 & 97.5 & 93.6 & 92.7 & 97.3 & 98.8 \\ Tennis court & 80.2 & 80.1 & 84.5 & 92.4 & 87.6 & 93.7 & 97.8 & 96.4 \\ Basketball court & 70.6 & 82.9 & 74.6 & 82.9 & 87.1 & 96.7 & 84.8 & 92.4 \\ Ground track field & 83.4 & 94.0 & 88.1 & 98.8 & 99.6 & 98.4 & 98.4 & 99.5 \\ Harbor & 67.6 & 78.1 & 71.6 & 80.3 & 99.9 & 99.7 & 90.4 & 84.0 \\ Bridge & 83.4 & 61.3 & 87.4 & 93.0 & 86.2 & 71.5 & 89.2 & 94.3 \\ Vehicles & 72.2 & 76.1 & 76.3 & 86.4 & 89.9 & 91.6 & 90.3 & 93.8 \\ Mean AP & 78.1 & 81.5 & 82.3 & 91.1 & 91.5 & 93.4 & 93.3 & 94.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the KCFS-YOLOv5 with the common algorithms on the NWPU VHR-10 dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & _Map_ (\%) & \(P\) (\%) & _R\_c (\%) & _P (M)_ & _FPS_ \\ \hline
**YOLOv5** & 91.08 & 91.08 & 86.26 & 7.072 & 54.3 \\
**+K-means++** & 91.71 (+0.63) & 95.86 & 87.82 & 7.156 & 52.1 \\
**+CA** & 93.11 (+1.40) & 93.44 & 88.22 & 7.278 & 49.8 \\
**+Improved fusion neck network** & 93.97 (+0.86) & 93.51 & 89.33 & 7.341 & 41.3 \\
**+SieU(Improved Algorithm)** & 94.21 (+0.24) & 94.20 & 89.42 & 7.341 & 43.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: The simulation results of adding each module to YOLOv5 in turn on NWPU VHR-10 dataset. Based on the above data, the improved object detection method improves the detection accuracy of each object in the RSOD dataset. Compared with the current advanced object detection method, it also has the highest detection accuracy, and can be competent for multi-size object detection tasks with excellent practicability. The detection results are illustrated in Figure 14.  
The KCFS-YOLOv5 also has excellent detection performance on the UCAS-AOD-CAR dataset. Figure 15 shows its detection results on the UCAS-AOD-CAR dataset.