The increasing prevalence of unmanned aerial vehicles (UAVs), commonly known as drones, has generated a demand for reliable detection systems. The inappropriate use of drones presents potential security and privacy hazards, particularly concerning sensitive facilities. To overcome those obstacles, we proposed the concept of MultiFeatureNet (MFNet), a solution that enhances feature representation by capturing the most concentrated feature maps. Additionally, we present MultiFeatureNet-Feature Attention (MFNet-FA), a technique that adaptively weights different channels of the input feature maps. To meet the requirements of multi-scale detection, we presented the versions of MFNet and MFNet-FA, namely the small (S), medium (M), and large (L). The outcomes reveal notable performance enhancements. For optimal bird detection, MFNet-M (Ablation study 2) achieves an impressive precision of 99.8\%, while for UAV detection, MFNet-L (Ablation study 2) achieves a precision score of 97.2\%. Among the options, MFNet-FA-S (Ablation study 3) emerges as the most resource-efficient alternative, considering its small feature map size, computational demands (GFLOPs), and operational efficiency (in frame per second). This makes it particularly suitable for deployment on hardware with limited capabilities. Additionally, MFNet-FA-S (Ablation study 3) stands out for its swift real-time inference and multiple-object detection due to the incorporation of the FA module. The proposed MFNet-L with the focus module (Ablation study 2) demonstrates the most remarkable classification outcomes, boasting an average precision of 98.4\%, average recall of 96.6\%, average mean average precision (mAP) of 98.3\%, and average intersection over union (IoU) of 72.8\%. To encourage reproducible research, the dataset, and code for MFNet are freely available as an open-source project: github.com/ZeeshanKaleem/MultiFeatureNet.

翻译：随着无人机（UAV）的日益普及，对可靠检测系统的需求也随之产生。无人机的滥用可能对敏感设施等构成潜在的安全与隐私威胁。为克服这些障碍，我们提出了MultiFeatureNet（MFNet）的概念，该方案通过捕获最集中的特征图来增强特征表征。此外，我们提出了MultiFeatureNet-特征注意力（MFNet-FA）技术，该技术能自适应地对输入特征图的不同通道进行加权处理。为满足多尺度检测要求，我们推出了MFNet和MFNet-FA的小型（S）、中型（M）和大型（L）版本。实验结果表明了显著的性能提升。在最佳鸟类检测任务中，MFNet-M（消融研究2）实现了99.8%的惊人精确率；而在无人机检测任务中，MFNet-L（消融研究2）的精确率得分为97.2%。综合考虑小特征图尺寸、计算需求（GFLOPs）及运行效率（帧/秒），MFNet-FA-S（消融研究3）成为最节省资源的选项，尤其适用于性能受限的硬件部署。此外，得益于FA模块的引入，MFNet-FA-S（消融研究3）在快速实时推理和多目标检测方面表现突出。配备聚焦模块的MFNet-L（消融研究2）在分类任务中展现出最佳性能，其平均精确率为98.4%，平均召回率为96.6%，平均均值平均精度（mAP）为98.3%，平均交并比（IoU）为72.8%。为推动可重复研究，MFNet的数据集与代码已作为开源项目免费提供：github.com/ZeeshanKaleem/MultiFeatureNet。