ADMNet: Anti-Drone Real-Time Detection and Monitoring

Abstract

We propose a lightweight, effective, and efficient anti-drone network, namely ADMNet, for visually detecting and monitoring unfriendly drones with a constrained view field, flying against a complex environment. We merge an SPP module to the first head of YOLOv4 to improve accuracy and perform network compression to reduce inference latency and model size. To compensate for the accuracy loss caused by condensation, we propose an SPPS module and a ResNeck module for the neck of the network and implement an effective attention module for the backbone. Eventually, we present an accurate and compact ADMNet with barely 3.9 MB, ensuring low computational cost and real-time detection. Our method achieves state-of-the-art performance on three challenging real-world datasets (Average Precision @0.5IoU): Det-Fly 96.2%, NPS-Drones 92.0%, and TIBNet 89.7%. The throughput is higher than the prior work, in addition to its superior performance. The comparative testing in real-world scenarios proves that our method exhibits strong reliability and generalization ability. Deploying the network on drone onboard edge-computing devices enables real-time detection and monitoring of flying drones, highlighting the portability and viability of the ADMNet.