# A Efficient and Accurate UAV Detection Method Based on YOLOv5s
## 6 Conclusions  
This article introduces an efficient and accurate method for detecting UAVs, termed EDU-YOLO, which is based on the YOLOv5s architecture. This approach utilizes lightweight feature extraction modules, balanced feature fusion modules, and a novel EloU loss function to enhance the detection accuracy and convergence speed of the network. EDU-YOLO employs the ShuffleNetV2 and CA mechanisms to construct a lightweight backbone network and leverages a Bi-FPN and Ghost Conv to optimize feature fusion. These innovations significantly improve processing speed and model efficiency, making it suitable for possible deployment on embedded platforms.  
Although ShuffleNetV2 excels in reducing computational costs and model parameters, it may underperform compared to larger or deeper network models such as ResNet or DenseNet when tasked with high-complexity images or high-accuracy requirements. CA mechanisms enhance the model's feature extraction capabilities but may also increase the computational complexity and inference time, particularly under conditions of extreme input variability or complex backgrounds. The Bi-FPN enhances the semantic information of features, improving performance; however, its complexity might slow down network inference speeds. Furthermore, tuning and training Bi-FPN parameters could demand substantial computational resources and time. Ghost Convolution optimizes the model by reducing parameters and computational load but may sacrifice accuracy when handling complex image content requiring advanced feature representations. In summary, although EDU-YOLO has significantly improved network efficiency and detection speed, its performance in UAV detection under extreme conditions (such as very low light or highly dynamic backgrounds) still requires further validation. Moreover, while the model has made progress in detecting small targets, its ability to balance detection accuracy and false positive rates in complex environments still needs optimization. Additionally, despite high prediction accuracy, the confidence levels are moderate, which also requires improvement  
Figure 9: Visualization detection results of the EDU-YOLO.  
in future research. For the definitions of abbreviations and symbols used in this document, please refer to Supplementary Table S1.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14156398/s1](https://www.mdpi.com/article/10.3390/app14156398/s1)]([https://www.mdpi.com/article/10.3390/app14156398/s1](https://www.mdpi.com/article/10.3390/app14156398/s1)), Table S1: Acronyms Glossary; Table S2: Symbols Meaning.  
Methodology, T.W. and Y.F.; software, Q.J. and T.W.; validation, T.W., C.Z. and S.S.; data curation, C.Z., W.Q., and S.S.; writing--original draft preparation, T.W. and Q.J.; writing--review and editing, T.W. and Y.F.; visualization, S.S., T.W. and Y.F.; supervision, C.Z. and Y.F.; project administration, Y.F.; funding acquisition, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Projects of the Foundation Strengthening Program, grant number 2023-JCJC-JJ-0604.  
Not applicable.  
The data presented in this study are available on request from the corresponding author (privacy).  
The authors declare no conflict of interest.