# Object Detection in Drone Imagery via Sample Balance Strategies and Local Feature Enhancement
## 5 Conclusions  
For scenarios that require object detection from drones, the training datasets are not as rich as those from the ground-based dataset, e.g., ImageNet. In addition, the detection tasks needed mostly focus on small objects. In order to improve the performance of the detector therefore, most studies usually try to improve the aspects of loss design, training sample selection, feature enhancement and so forth. In this paper, we propose an object detection framework for drone scenarios that uses Sample Balance Strategies and a weight-GANs sub-network to improve detection performance. In terms of the selection of training samples, we propose the Sample Selection Strategies method, which balances positive and negative samples, and easy and hard samples based on ATSS (Adaptive Training Sample Selection). Validated through experiments, our method's performance has been shown to surpass that of ATSS. Furthermore, in terms of the enhancement of features, we introduce weight-GAN to generate super-resolved features and enhance the representation of small objects with center-ness weights based on Perceptual GANs. Using experiments, we can observe that our method performs in a better way than simply enlarging the scale of the image and using Perceptual GANs. Finally, we compare our proposed method to the benchmark and again achieve a better performance in UAV object detection scenarios.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Method** & \(\mathbf{AP\) [\%]} & \(\mathbf{AP_{50}}\) [\%] & \(\mathbf{AP_{75}}\) [\%] & \(\mathbf{AR_{1}}\) [\%] & \(\mathbf{AR_{10}}\) [\%] & \(\mathbf{AR_{100}}\) [\%] & \(\mathbf{AR_{500}}\) [\%] \\ \hline CornetNet & 23.43 & **41.18** & 25.02 & 0.45 & 4.24 & 33.05 & 34.23 \\ FPN & 22.06 & 39.57 & 22.50 & 0.29 & 3.50 & 30.64 & 31.61 \\ FCOS + ATSS & 23.39 & 39.52 & 26.34 & 0.41 & 3.45 & 31.49 & 31.49 \\ Perceptual GANs & 21.32 & 38.34 & 25.11 & 0.27 & 3.55 & 32.11 & 32.07 \\ Ours w/SBS & 23.42 & 39.77 & 26.36 & 0.47 & 3.98 & 31.65 & 31.65 \\ Ours w/WGAN & 23.34 & 39.56 & 26.18 & 0.25 & 3.03 & 32.71 & 32.69 \\ Ours w/SBS + WGAN & **24.17** & 40.98 & **26.71** & **0.51** & **4.29** & **33.19** & **33.19** \\ \hline \hline \end{tabular}
\end{table}
Table 4: A table for calculating the training AP and AR of different models for the same dataset (Visdrone2020-dev).  
Figure 11: The results of our detection framework on the VisDrone2020 benchmark.  
**Author Contributions:** Conceptualization, X.H. and H.X.; methodology, X.H.; software, X.H. and K.Z.; validation, X.Y., J.X. and K.Z.; formal analysis, X.Y. and W.H.; investigation, X.H. and W.H.; resources, X.H.; data curation, X.Y., J.X. and K.Z.; writing original draft preparation, X.H., K.Z. and J.X.; writing review and editing, H.X. and J.X.; visualization, X.Y. and X.H.; supervision, K.Z.; project administration, X.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data available on request due to restrictions e.g., privacy or ethical. The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** The authors declare no conflict of interest.