# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 5 Simulation Results
### Effects of the New Tiny Object Detection Head  
Compared with the conventional YOLOv5 framework, the new tiny object detection head is inclined to extract and detect features of tiny objects. Therefore, we embed the new tiny object detection head into the neck network of the YOLOv5 to further achieve the feature of tiny objects, and tested the improved algorithm on three different aerial remote sensing datasets. We introduced the results in Table 5.  
The new tiny object detection head improves the extraction and detection ability of tiny object features by UP-sampling. Therefore, the improvement effect is obvious for the dataset rich in tiny objects. The three datasets used in the experiment are all aerial remote sensing datasets, which contain a plenty of tiny objects such as aircraft and ships. It can be seen that the addition of the new tiny object detection improves the detection accuracy of the three datasets. Therefore, we decided to embed the new tiny object detection in the improved YOLOv5 object detection method. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**NWPU VHR-10**} & \multicolumn{5}{c}{**RSOD**} & \multicolumn{5}{c}{**UCAS-AOD-CAR**} \\  & _Map (\%)_ & \(P_{s}\) (\%) & \(R_{e}\) (\%) & _FPS_ & _Map (\%)_ & \(P_{s}\) (\%) & \(R_{e}\) (\%)_ & _FPS_ & _Map (\%)_ & \(P_{s}\) (\%)_ & \(R_{e}\) (\%)_ & _FPS_ \\ \hline YOLOv5 & 91.08 & 91.08 & 86.26 & 54.3 & 90.67 & 90.59 & 89.56 & 56.5 & 86.48 & 85.30 & 83.36 & 60.6 \\ YOLOv5+BIFPN & 91.92 & 92.77 & 88.67 & 52.4 & 92.29 & 91.19 & 90.85 & 54.9 & 88.66 & 89.75 & 84.38 & 55.6 \\ \hline \end{tabular}
\end{table} 
Table 4: Effects of the bidirectional feature pyramid network framework on three different datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**NWPU VHR-10**} & \multicolumn{5}{c}{**RSOD**} & \multicolumn{5}{c}{**UCAS-AOD-CAR**} \\  & _Map (\%)_ & \(P_{s}\) (\%) & \(R_{e}\) (\%) & _FPS_ & _Map (\%)_ & \(P_{s}\) (\%) & \(R_{e}\) (\%)_ & _FPS_ & _Map (\%)_ & \(P_{s}\) (\%)_ & \(R_{e}\) (\%)_ & _FPS_ \\ \hline YOLOv5 & 91.08 & 91.08 & 86.26 & 54.3 & 90.67 & 90.59 & 89.56 & 56.5 & 86.48 & 85.30 & 83.36 & 60.6 \\ YOLOv5+CA & 93.00 & 94.18 & 88.27 & 50.0 & 94.17 & 95.27 & 91.87 & 51.0 & 87.80 & 85.54 & 84.24 & 52.1 \\ \hline \end{tabular}
\end{table} 
Table 3: Effects of Coordinate Attention module on three datasets. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**NWPU VHR-10**} & \multicolumn{5}{c}{**RSOD**} & \multicolumn{5}{c}{**UCAS-AOD-CAR**} \\  & _Map (\%)_ & \(P_{s}\) (\%) & \(R_{e}\) (\%) & _FPS_ & _Map (\%)_ & \(P_{s}\) (\%) & \(R_{e}\) (\%)_ & _FPS_ & _Map (\%)_ & \(P_{s}\) (\%)_ & \(R_{e}\) (\%)_ & _FPS_ \\ \hline YOLOv5 & 91.08 & 91.08 & 86.26 & 54.3 & 90.67 & 90.59 & 89.56 & 56.5 & 86.48 & 85.30 & 83.36 & 60.6 \\ YOLOv5+**New tiny** & 93.47 & 93.58 & 89.04 & 45.2 & 91.79 & 93.30 & 88.83 & 46.7 & 87.92 & 87.52 & 83.51 & 48.8 \\ \hline \end{tabular}
\end{table} 
Table 5: Effects of the new tiny object detection head on three different datasets. To further explore the improvement effect of the improved neck network on the accuracy, we fused the Bidirectional Feature Pyramid Network(BiFPN) and the new tiny object detection head into the conventional YOLOv5 algorithm, and simulated the improved fusion neck network in the above dataset. Table 6 is the results. The improved fusion neck network embedded with the conventional YOLOv5 network achieves the best detection performance which is better than adding the Bidirectional Feature Pyramid Network(BiFPN) framework and the new tiny object detection head alone. Meanwhile, the \(FPS\) of the improved algorithm is not significantly reduced. Therefore, the YOLOv5 algorithm with the improved fusion neck network has excellent performance. To further measure the practical value of the improved neck network, we count the parameters of the above improved fusion neck network.