# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 7 Conclusions  
We aimed at the characteristics of object detection methods in aerial remote sensing images, such as intensive distribution, complex background, and poor generality. We carried out a series of improvements based on YOLOv5, tested the improved model on three datasets, and achieved excellent detection results. Finally, we proposed an advanced high-precision aerial object detection method: KCFS-YOLOv5.  
To achieve excellent detection performance, we first used the K-means++ clustering algorithm to optimize the center of the anchor, which improves the detection accuracy of YOLOv5. Second, the CA attention mechanism module is embedded to strengthen feature extraction ability, retain more semantic information and reduce the interference of complex backgrounds. Third, the neck network is optimized by adding Bidirectional Feature Pyramid Network (BiFPN) and new tiny object detection head, which can enhance the feature fusion ability in different dimensions of the neck feature fusion network to improve the detection accuracy. Finally, the loss function is replaced with SloU Loss to improve the fitting degree of the predicted box and the ground truth box, and reduce the influence of the deviation thereon.  
Although our KCFS-YOLOv5 object detection method realizes high-precision detection in aerial remote sensing images, compared with the conventional YOLOv5 method, it has slightly more calculation parameters. In future research, we will try to embed more lightweight modules and residual frameworks into the KCFS-YOLOv5 object detection method, so as to reduce the network scale and improve its detection accuracy.  
Conceptualization, Z.T.; methodology, Z.T. and J.H.; software, Z.T. and W.N.; validation, Z.T., J.H., Y.Y. and W.N; formal analysis, Z.T.; investigation, Z.T.; resources, J.H. and Y.Y.; data curation, Z.T. and W.N.; writing--original draft preparation, Z.T.; writing--review and editing, Z.T., J.H., Y.Y. and W.N.; visualization, Z.T. and W.N.; project administration, J.H. and Y.Y.; funding acquisition, J.H. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The three datasets that support this study are openly available online.  
RSOD: [22], NWPU VHR-10: [23], UCAS-AOD-CAR: [24].  
The authors would like to thank the anonymous reviewers for their constructive suggestions, which comprehensively improved the quality of the paper.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} D-CNN & Deep Convolutional Neural Network \\ R-CNN & Region Convolutional Neural Network \\ Fast R-CNN & Fast Region-Based Convolutional Neural Network \\ Faster R-CNN & Faster Region-Based Convolutional Neural Network \\ SSD & Single Shot MultiBox Detector \\ YOLO & You Only Look Once \\ CSP & Cross Stage Partial \\ FPN & Feature Pyramid Network \\ PAN & Path Aggregation Network \\ NMS & Non Maximum Suppression \\ CA & Coordinate Attention \\ BiFPN & Bidirectional Feature Pyramid Network \\ SE & Squeeze and Excitation \\ NAM & Normalization-based Attention Module \\ ECA & Efficient Channel Attention \\ DIoU & Distance-IoU \\ CIoU & Complete IoU \\ GIoU & Generalized Intersection over Union \\ SloU & Convolutional Block Attention Module \\ UAV & Unmanned Aerial Vehicle \\ SAHI & Slicing Aided Hyper Inference \\ STN & Spatial Transformer Networks \\ \end{tabular}