# An Improved YOLO Model for UAV Fuzzy Small Target Image Detection
## 4 Experiment and Discussion
### Ablation Experiment  
We used our in-house UAV and Artemisia dataset for subsequent experiments to emphasize the detection of small objects and low resolution in experiments. A set of ablation experiments were designed to test the performance of different modules on the network to verify the effectiveness of the proposed improvements for small-object and low-resolution detection. The modules included the SPD-Conv module, attention mechanism, transpose convolution module, and alteration of the loss function.  
Detection performance comparison:  
* Detection results of Original YOLOv5s are shown in Figure 16:  
Figure 14: Recall comparison results.  
Figure 13: Precision comparison results.  
Figure 15: Loss comparison results.  
* Detection results with SPD-Conv module are shown in Figure 17:
* Detection results with CA attention mechanism are shown in Figure 18:
* Detection results with transposed convolution are shown in Figure 19:  
Figure 16: Detection results of Original YOLOv5s.  
Figure 17: Detection results with SPD-Conv module.  
Figure 18: Detection results with CA attention mechanism.  
The results shown in the graph indicate that the detection capabilities of the improved module are stronger than those of the original YOLOv5s for low resolution and small objects with an identical validation image. As observed in the graph, the detection confidence of the original YOLOv5s for small objects visible to the naked eye was 0.45. However, all the values of this parameter for the improved module were greater than 0.7. In addition, the improved model exhibited better detection performance for small objects at the edges of the image, whereas the original model had a higher rate of missed detection. To summarize, the improved model proposed in this study outperformed the original YOLOv5s model in terms of detection capabilities for low-resolution and small objects, along with a reduction in missed detections.  
The specific performance of the model obtained by combining various improvement modules is presented in Tables 4 and 5.  
As shown in the table, adding the SPD-Conv, CA mechanism, and transpose convolution modules individually resulted in nonsignificant improvements in the model's identification capabilities. The increase in mAP was large when only the SPD-Conv was used with the CA mechanism. However, the improvement in the model's identification accuracy was nonsignificant. Using only SPD-Conv with transpose convolution resulted in decreased model recall rate and precision, although a certain improvement in mAP was achieved. The model's recall rate and precision improved when the CA mechanism was used with transpose convolution. However, mAP decreased marginally. The model's recall rate, precision, and mAP improved to varying degrees after the three types of improvements and the loss improvement were added. Compared with the original YOLOv5s, the number of network training layers increased by 47 layers, the number of model parameters  
Figure 19: Detection results with transposed convolution.  
Figure 20: Detection results with all modules added.  
increased by 2.89 M, the number of model floating-point operations increased by 24.2 G, and the inference time increased by 4.1 ms. Compared with the original network, the amount and reasoning time increased. Although the real-time performance of model detection decrease, the decrease has not been large. Moreover, the improved model significantly improved the accuracy of small-object detection in UAV fuzzy images. The improved model mAP, precision, and recall were increased by 14.96%, 6.24%, and 7.21%, respectively. Training results of different combinations of mAP, precision, recall and loss are shown in Figures 21-24:  
Plots of the training results of different combinations of mAP, precision, recall, and loss reveal that the improved model in this study exhibited improvements in these parameters. It is also evident that this model performed better than the original model on low-resolution and small-object datasets.