# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 4 Experiments
### Implementation and Performance Evaluation of Weight-GAN Subnet  
In order to verify the effectiveness of the weight-GAN method, we compare our method against several other feature enhancement methods on their ability to detect objects through drone vision. As shown in Table 2, we train and test the models on the datasets, which are all generated from Visdrone2020 as shown in Figure 6. Our method can achieve the state-of-the-art performance, and improve by 5.46% for Large Scale Images, by 3.91% for SRGAN [45], by 3.59% for ESRGAN [56] and by 1.23% for Perceptual GANs. The Large Scale Images method represents the model trained using high-resolution images by directly increasing the scale of the input image, e.g., \(\times\)4. The SRGAN and ESRGAN methods provide the generative network for the image super-resolution (SR). Perceptual GAN is able to generate super-resolved representations for small objects, but it has a lower accuracy than our method. Thus it proves that the method we have proposed to enhance local features is effective for the detection of objects in drone scenarios.  
We also visualize the intermediate results of the super-resolved features generated by weight-GAN, as shown in Figure 10. The first column shows the candidate objects in images captured by drones, while the second column and final column display the features of small objects and large objects, respectively. Because of the residual block in weight-GAN, the third column shows the residual representation features that are generated by the residual block with center-ness weights. Next, in the fourth column, the super-resolved features are generated by weight-GAN.  
The extracted features of small objects are easy to be disturbed by the noise of contexts. Thus, we extract the basic feature map based on FPN with multi-level layers to generate the small object feature representation. We utilize four \(H\times W\times 256\) fully convolutional layers to regress the class and bounding box position in the following classification branch and regression branch. \(H\times W\) is the height and width of feature maps. The detector we proposed based on FCOS directly views the locations as training samples rather than  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Method** & **Top1—Accuracy [\%]** \\ \hline Large Scale Images & 65.98 \\ SRGAN & 67.53 \\ ESRGAN & 67.85 \\ Perceptual GANs & 70.21 \\ Ours w/weight-GAN & **71.44** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of detection performance with different methods on weight-GAN sub-network datasets generated from Visdrone2020.  
Figure 9: Comparative results of FPN+RPN, FCOS w/ATSS and our w/SBS method.  
anchor boxes, similar to FCNs for segmentation tasks. Thus, the enhancement of local features and the suppression of edge features for one small object helps be regressed and classified via fully convolutional layers. We can observe that the result from the super-resolved features is similar to the result from the large objects, and in addition, that the center-ness of learned features are enhanced and edges are suppressed. Thus the method we proposed achieves a better performance.