## 5 Experimental Results
### Result Analysis  
Since this experiment adopts the pre-training method, the training times of the algorithm before and after the improvement are 100-200 times, respectively, to achieve the fitting effect. The evaluation index system of a coco is used in this paper, and the specific training results are shown in Table 2.  
It can be seen from Table 2 that the improved GC-Faster-RCNN in this paper has a greater improvement in accuracy compared with the original algorithm under the same conditions. The specific differences between the two methods are shown in Figure 7 below.  
In Figure 7, the result on the left column is Faster-RCNN, and on the right is the GC-Faster-RCNN. It can be seen from the above figures that in the cloudy background, the UAV can be effectively detected, and the accuracy is higher than the Faster-RCNN.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Backbone (50 Layers)** & **mAP0.5/\%** & **mAP0.75/\%** \\ \hline \multirow{2}{*}{Faster-rcnn} & Resnet & 71.5 & 27.6 \\  & Resnet + Y & 89.8 & 38.2 \\ \multirow{2}{*}{GC-Faster-rcnn} & Resnet + Y + Gse & 93.2 & 44.0 \\ \multirow{2}{*}{GC-Faster-rcnn} & Resnet + Y & 91.3 & 38.2 \\  & Resnet + Y + Gse & 94.8 & 41.9 \\ \hline \hline \end{tabular} Note: Y in the table is pre-training, Gse is the improved attention mechanism, GC refers to the algorithm after changing to the Resnex50 network.  
\end{table}
Table 2: Comparison of detection accuracy of various network structures.  
Figure 7: _Cont._  
In the image taken by the mobile phone, because the color of the target and the background are very similar, and the target is smaller than the target in the dataset, and the leaves on the surrounding trees will also cause some detection interference to the detection, resulting in the coordinate information of the a priori frame potentially matching  
Figure 7: Comparison chart of experimental results. (**a**) Faster-rcnn; (**b**) GC-Faster-rcnn. The boxes in these figures are the detection accuracy.  
other distractors during regression, so it is difficult to accurately predict the correct target. Therefore, the detection accuracy of the model for such scenes is reduced. The training loss and map curves in this paper are shown in Figure 8.  
According to the curve, the convergence of training and the decline in loss can be completed quickly with the blessing of pre-training, and the convergence can generally be completed in about 100 rounds.  
It can be seen from Figure 8 that the improved GC-Faster-RCNN algorithm has a significant improvement on the map compared to the original algorithm, and the curve is more stable. Its accuracy can reach 94.8%.  
Although the accuracy of fast RCNN can be stabilized in the 50 th round, the subsequent overall accuracy still fluctuates greatly, and the detection objects of the original algorithm are all large, medium, and small targets. Therefore, the accuracy of small target detection is not as good as that of the algorithm only for small targets.  
It is worth mentioning that the loss of the original algorithm decreases very fast, but it is still unstable until the training reaches 100 rounds. It reflects that the regression accuracy of its a priori frame fluctuates greatly and is not as stable as GC-Faster-RCNN.  
At the same time, during the training, it is found that the simplification of the high-level network can also improve the accuracy of small targets, because the deep semantic information extracted by the high-level feature network is aimed at the characteristics of  
Figure 8: Comparison chart of training and loss curves. (**a**) Faster-rcn; (**b**) GC-Faster-rcnn.  
medium and large targets, and the characteristic information of small targets has difficulty performing well in the high-level network after layer-by-layer network extraction. Therefore, the detection network of small targets can focus on the optimization of the low-level network, and the high-level can reduce it appropriately according to requirements.