SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

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. Consequently, a critical necessity revolves around the development of a proficient system with the capability to precisely identify UAVs and other flying objects even in challenging scenarios. Although advancements have been made in deep learning models, obstacles such as computational intricacies, precision limitations, and scalability issues persist. To overcome those obstacles, we proposed the concept ofMultiFeatureNet(MFNet), a solution that enhances feature representation by capturing the most concentrated feature maps. Additionally, we presentMultiFeatureNet-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%.