# A Efficient and Accurate UAV Detection Method Based on YOLOv5s
## 5 Discussion  
The model under discussion leverages the ShuffleNetV2 architecture, which is renowned for its efficiency in managing computational resources, particularly in mobile and embedded applications. By incorporating a coordinate attention mechanism, the model further refines its ability to focus on more informative features during the extraction process. This mechanism selectively emphasizes features that are more relevant to the task, thereby improving the representational power of the network without a significant increase in computational demands.  
To enhance the integration of features at different scales, the model integrates a Bi-FPN. This innovative approach differs from traditional unidirectional pyramids by allowing for the flow of information both upward and downward across the scales. This bidirectional flow enables more effective feature-level interactions, leading to richer and more semantically meaningful feature maps. Moreover, the algorithm assigns learnable weights to each input feature at different levels of the pyramid, optimizing the contribution of each feature during fusion. This learning-based approach to weighting allows the model to dynamically adapt to the most useful features during training, leading to a more balanced and effective integration. Lastly, the model adopts the EIoU bounding box loss function to improve the precision in bounding box regression tasks. Traditional IoU-based loss functions often face challenges such as scale sensitivity and misalignment issues, which can degrade the localization accuracy. EIoU addresses these issues by incorporating additional terms that account for the central point distances between the predicted and actual bounding boxes, enhancing the alignment accuracy. The use of EIoU facilitates faster and more stable convergence of the network during training and improves the model's ability to precisely locate objects, which is crucial for applications requiring high localization accuracy, such as autonomous driving or precision agriculture. The visualization detection results of the EDU-YOLO is shown in Figure 9.  
Together, these advancements in feature extraction, feature fusion, and bounding box regression contribute to a robust model that not only performs tasks efficiently but also achieves high precision in detecting and localizing objects, making it suitable for real-time applications where both speed and accuracy are critical.  
We conducted a detailed and thorough analysis of the results from three sets of experiments. Firstly, in the ablation experiments, we observed that replacing the backbone network with Shufflenetv2 and introducing Ghost Conv modules led to a slight decrease in detection accuracy but significantly reduced computational complexity, greatly improving detection speed, showcasing a balance of performance and speed enhancement. Secondly, with the incorporation of the CA attention mechanism and Bi-FPN network module, the model excelled in feature fusion and information extraction, significantly enhancing detection accuracy. Lastly, in the experiments where the loss function was changed from CIoU to EIoU, we observed an increase in recall rate and mAP, further enhancing the overall model performance. These experimental results validate the effectiveness and superiority of the EDOU-YOLO model in small target detection tasks. This study contributes to the real-time and efficient detection of UAVs, facilitating early warning systems and enhancing the security measures for high-value targets.