SafeSpace MFNet: Precise and Efficient MultiFeature Drone Detection Network

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 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.