# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 4 Experiments and Results
### Ablation Experiments
Among them, RFCAConv and C2 GELAN can significantly improve the accuracy in the network, and the resulting decrease in FPS is negligible. However, the CAMSPD module has led to a considerable decrease in FPS while improving accuracy, having a greater impact on the speed. As shown in Figure 6, the changes in the training process metrics of the improved model and the base model are compared and analyzed. Specifically, the peak performance of the improved model reaches a higher level, and its performance improvement is obvious compared to the base model. Throughout the entire training process, the improved model is also more stable, indicating that it is more adaptable to the training data and can effectively capture target features at different stages, thus reducing uncertainty in the training process. The improved model outperforms the base model in all indexes during the training process, achieving not only higher peak performance but also better stability and convergence effects. Figure 7 shows the heat map visualization effect of the base model and the improved model. The first four rows in Figure 7 are pictures from the validation set of VisDrone2019, and the last three rows are pictures from the validation set of TinyPerson, from which it can be seen that a significant advantage is held by the improved model in enhancing the detection of small targets. The heat map visualizes the detection performance of the model and reveals the effect of model improvement. (1) The basic YOLOv8n model is focused only on the large targets in the scene, while the improved model is capable of effectively detecting both large targets and small targets in the scene. This is reflected in the larger range and greater number of target areas detected in the heat map of the improved model. (2) It can be seen that the target area in the heat map of the improved model is displayed with a more prominent color, indicating that a stronger perception of the target is achieved by the improved model, which can effectively suppress background noise interference and highlight the target features. In order to further evaluate the detection performance of the improved model, different images from the VisDrone2019 dataset and TinyPerson dataset are selected for detection, and the detection effect is shown in Figures 8 and 9. The first line in Figures 8 and 9 display the detection effect of YOLOv8n, while the second line displays the detection effect of the improved model. The numbers of target detections in the first row of Figure 8 are 81, 87 and 89, respectively, while those in the second row are 104, 89 and 106, respectively. As seen in Figure 8, for targets in different scenarios, more small targets are detected by the improved model, such as the MOTOR in Figure 8b and the MOTOR and PEDESTRIAN in Figure 8c. Fewer misdetection cases are present in the improved model, as the base model misdetects people as motorbikes in Figure 8a and buses as trucks in Figure 8c. From each scenario, it can be seen that higher detection confidence is held by the improved model for the same target. The numbers of target detections in the first row of Figure 9 are 74, 29 and 82, respectively, while those in the second row are 143, 27 and 143, respectively. It can be seen from Figure 9 that the improved model can detect more targets and has higher confidence for the same detected targets. In the first two scenes, the original model has obvious misdetection cases. For example, it misdetects \"sea person\" as \"earth person\" and recognizes the background as \"sea person\", etc. Relatively speaking, the improved model has a higher accuracy. In the third scene, both the original model and the improved model have obvious misdetection cases, but the improved model has fewer false detections and a higher recognition recall rate. The improved model in this paper can effectively improve the detection of small and tiny targets by unmanned aerial vehicles. Figure 6: Comparison chart of indicator changes in the training process of the improved model versus the basic model.