# UAV Image Small Object Detection Based on RSAD Algorithm
## 4 Results
### Comparison Experiment  
The above experiments proved that the model in this study is better than the SSD model. In order to further explore the advantages and disadvantages of the RSAD object detection model proposed in this study with the existing excellent object detection models, this study trained a variety of excellent object detection models under the same experimental environments and datasets, including: the Faster R-CNN [44], the Cascade R-CNN [45], Retain-Net [46], Center-Net [47], YOLOv5s [48], and YOLOv8n [49], and the AP values for each object and the mAP values for all categories were obtained on the test dataset. Table 3 lists the results of each model on the test set. From the table, it can be seen that the model proposed in this study achieved the highest mAP value of 30.5% for the image input size of 300 \(\times\) 300, which gives the best overall detection performance for UAV images. The optimal AP value is achieved for the following values: bicycle, van, truck, tricycle, awning tricycle, bus single object. Slightly poorer detection accuracy was achieved for Pedestrian and Car, probably due to the high number of instances of Pedestrian and Car in the dataset, and the modulation factor of the Focal Loss function reduced the model's focus on these two categories, resulting in lower APs for these two object categories. The AP values on People and Motor individual objects are second only to the two-stage object detection model, but the detection speed is far superior to them, making it easier to be deployed on UAVs for real-world applications. Figure 8: The mAP of ablation experiments. Figure 9: Comparison of visualization feature heatmaps. (**a**) Original image. (**b**) Feature map of the SSD model. (**c**) Feature map of the proposed model. The above experiments proved that the RASD model proposed in this study has the best integrated detection performance for UAV images compared to other excellent models when the image input is 300 \(\times\) 300. In order to more intuitively reflect the excellent performance of the RSAD model, the YOLOv5 model, which is second only to RSAD in terms of mAP value and has a similar detection speed to RSAD in Table 2, is selected for the comparison of detection results. Figure 10 shows the detection results of YOLOv5 and RSAD models in different environments as well as under different light conditions. From the Figure 10a, b, we can intuitively see that the YOLOv5 model has missed detection of objects in the image, while the RSAD model can accurately detect them. In Figure 10c, d, where the light is insufficient and the environment is more complex, the YOLOv5 model has missed detection due to the complex environment and insufficient light, but the RSAD model still maintains excellent detection performance and shows strong anti-interference ability. The detection results show that the RSAD model proposed in this study exhibits excellent detection performance for UAV images with a large number of small objects and large-scale transformations during daytime and nighttime, and RSAD can effectively suppress the interference of image background noise information, selectively mine important feature information, and effectively guide the UAV to complete the task of target detection in different environments.