SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network
Misha Urooj Khan, Mahnoor Dil, Muhammad Zeshan Alam, Farooq Alam, Orakazi, Abdullah M. Almasoud, Zeeshan Kaleem, Chau Yuen

TL;DR
This paper introduces MultiFeatureNet (MFNet) and its attention-enhanced version MFNet-FA for precise, efficient drone and bird detection across multiple scales, achieving high accuracy and real-time performance suitable for limited hardware.
Contribution
The paper presents a novel multi-feature detection network with adaptive channel weighting, optimized for multi-scale drone detection and resource efficiency, with open-source code and datasets.
Findings
MFNet-M achieves 99.8% precision for bird detection.
MFNet-L achieves 97.2% precision for UAV detection.
MFNet-FA-S offers a balance of efficiency and accuracy for real-time detection.
Abstract
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…
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Taxonomy
TopicsAdvanced Neural Network Applications · UAV Applications and Optimization · Video Surveillance and Tracking Methods
MethodsFeedback Alignment · Convolution
