# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 6 Discussion
### About the Backbone Network  
The backbone network extracts the texture information and position information in the feature map. To further enhance the feature extraction ability of the backbone network, many studies have been investigated to address these tricky challenges. [PERSON] et al. [28] integrated the SE attention mechanism module into YOLOv5 network to achieve the accuracy improvement of the high detection accuracy for low-altitude UAV objects. [PERSON] et al. [31] embedded ECA attention mechanism into YOLOX [17] network, proposed RS-YOLOX, and realized the high-precision detection of satellite remote sensing images. Based on the above research, we decided to embed CA attention mechanism module into the YOLOv5 backbone network to strengthen the feature extraction capacity of the network.  
We compared the detection performance with some advanced attention mechanisms (the results are shown in Tables 2 and 3). The CA attention mechanism module achieves the excellent detection results on the test dataset. The detection accuracy of YOLOv5 embedded with CA attention mechanism module is greatly improved compared with the conventional  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Evaluation Metrics** & **Faster-RCNN** & **YOLOv3** & **YOLOv4** & **YOLOv5** & **Ours** \\ \hline \multirow{8}{*}{**NWPU VHR-10**} & _Map_ (\%) & 81.39 & 82.29 & 86.31 & 91.08 & 94.21 \\  & Precision (\%) & 90.23 & 88.12 & 89.97 & 91.08 & 94.20 \\  & Recall (\%) & 85.36 & 84.43 & 86.68 & 86.26 & 89.42 \\  & parameters (M) & 60.684 & 61.949 & 62.725 & 7.072 & 7.341 \\  & Latency (_ms_) & 139.3 & 24.8 & 23.3 & 18.4 & 22.8 \\  & FPS & 7.2 & 40.3 & 42.9 & 54.3 & 43.9 \\ \hline \multirow{8}{*}{**RSOD**} & _Map_ (\%) & 82.88 & 84.73 & 86.62 & 90.67 & 95.27 \\  & Precision (\%) & 86.46 & 87.14 & 87.68 & 90.59 & 96.11 \\  & Recall (\%) & 85.41 & 86.18 & 86.59 & 89.56 & 93.05 \\  & parameters (M) & 60.684 & 61.949 & 62.725 & 7.072 & 7.341 \\  & Latency (_ms_) & 126.4 & 23.7 & 22.9 & 17.7 & 22.6 \\  & FPS & 7.9 & 42.2 & 43.7 & 56.5 & 44.2 \\ \hline \multirow{8}{*}{**UCAS-AOD-CAR**} & _Map_ (\%) & 84.54 & 81.66 & 85.03 & 86.48 & 90.13 \\  & Precision (\%) & 84.75 & 83.47 & 84.15 & 85.30 & 89.80 \\ \cline{1-1}  & Recall (\%) & 82.81 & 83.38 & 83.11 & 83.36 & 86.51 \\ \cline{1-1}  & parameters (_M_) & 60.684 & 61.949 & 62.725 & 7.072 & 7.341 \\ \cline{1-1}  & Latency (_ms_) & 114.8 & 23.1 & 22.3 & 16.5 & 21.4 \\ \cline{1-1}  & FPS & 8.7 & 43.3 & 44.8 & 60.6 & 46.7 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of the KCFS-YOLOv5 with the common algorithms on all three datasets.  
algorithm on three datasets. Therefore, we decided to embed the CA attention mechanism in the improved YOLOv5 backbone network.  
Although this method can improve the detection accuracy, it will reduce the detection speed. In the future, we will try to simplify this model. Many scholars have performed valuable research in this field. For example, [PERSON] et al. [53] added Mobilenet to the YOLO network to lightweight the detection network. However, this improved method will lead to the reduction in detection accuracy. Therefore, we do not adopt this method.