# An Improved YOLO Model for UAV Fuzzy Small Target Image Detection
## 5 Conclusions  
This study proposed an improved object-detection algorithm based on YOLOv5s. The objective was to enhance the detection capability of unmanned aerial vehicles for low-resolution and small objects. First, based on an experimental comparison and practical requirements, we selected YOLOv5s as the algorithm to be improved considering the differences in model size and performance. Then, we replaced the original convolution in the model with SPD-Conv convolution to enhance the network's feature extraction capability for low-resolution and small objects. Next, we integrated the CA mechanism into the YOLOv5s network structure to strengthen the feature extraction and improve the detection accuracy with small objects in blurry images. In addition, we replaced the nearest neighbor interpolation used for upsampling in the original network neck with transpose convolution to increase the receptive field of the neck and prevent issues such as low image resolution and loss of details at the end of the neck. Finally, we replaced the CIoU Loss with Alpha-IoU to accelerate the convergence of the model gradient. Currently, the recognition and tracking of small targets based on the UAV platform have a broad range of applications, but the images captured by UAVs in the air usually have characteristics such as blur and small resolution, so it is of great value to design an algorithm suitable for fuzzy small-target detection. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **mAP/\%** & **P/\%** & **R/\%** & **Latency/ms** & **Parameters/M** \\ \hline Yolov5s & 65.21 & 67.24 & 69.76 & 20.25 & 7.02 \\ \hline Yolov4 & 67.32 & 68.36 & 71.45 & 22.72 & 8.39 \\ \hline SSD & 81.23 & 74.21 & 74.49 & 25.16 & 8.63 \\ \hline Faster-RCNN & 79.85 & 77.49 & 76.49 & 30.25 & 13.71 \\ \hline RetinaNet [30] & 67.36 & 69.45 & 69.32 & 30.12 & 6.45 \\ \hline TPH-YOLOv5 [31] & 79.06 & 72.56 & 75.12 & 21.16 & 8.36 \\ \hline Fast-YOLOv4 [10] & 70.25 & 71.36 & 69.78 & 19.58 & 8.78 \\ \hline Improved YOLOv4 [9] & 68.36 & 68.51 & 67.12 & 17.15 & 6.32 \\ \hline ours & 80.17 & 73.45 & 76.97 & 24.35 & 9.91 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: mAP, precision, and recall values obtained by training conventional object detection algorithms. This study comprehensively considers the characteristics of blurred images and small target scenes in UAV recognition, and it uses the single-stage YOLOv5s algorithm to improve it. For the small-target recognition and tracking application of the UAV platform, a detailed comparison was performed with the latest algorithm in the current field in the experiment. Our improved algorithm had higher detection accuracy and lower false detection rates, effectively improving the detection accuracy of the UAV platform for fuzzy small targets and the reliability of small-target detection in UAV fuzzy images. Although there is a slight gap in the real-time performance of our algorithm in the latest technology comparison of real-time UAV target detection algorithms based on edge computing, our detection accuracy is higher than that of the latest related research algorithms, which is very meaningful. In addition, our improved method also provides a new idea and methodology for object detection problems in other related fields [11], with certain reference significance. In short, we believe that, through continuous optimization and improvement, UAV small-target detection technology will continue to develop toward higher accuracy and efficiency, bringing more possibilities and opportunities for UAV applications. The algorithm proposed in this study improved the mAP, recall, and precision compared with the original YOLOv5s algorithm, resulting in better detection performance. However, the increased detection performance was achieved at the cost of increased model complexity and parameters, which may hinder its deployment on certain embedded devices.