  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.
