QHNet: A Novel Quad-Head Network for Real-Time Detection of Intruding Drones

Abstract

The unlawful use of noncooperative drones, or unmanned aerial vehicles (UAVs), poses serious threats to public safety and societal security, necessitating robust monitoring solutions. However, the detection of drones, particularly those flying remotely, is often hindered by the limited accuracy in identifying small targets. Additionally, challenges related to insufficient lightweight design and complexities in practical deployment further exacerbate the issue. To address these challenges, we propose a quad-head network, QHNet, designed to provide scalable, adaptable, and efficient drone detection across diverse scenarios. QHNet is available in five scalable model sizes—Nano (N), Small (S), Medium (M), Large (L), and Extra Large (X)—ensuring real-time detection tailored to varying resource constraints and operational requirements. The core of QHNet lies in its innovative quad detection head (QDH), which introduces an additional detection layer to perform secondary feature extraction for small targets, enhancing detection precision. Furthermore, the architecture integrates adaptive spatial feature fusion to improve scale invariance and overall detection accuracy. To further optimize performance, QHNet incorporates a four-scale feature fusion network (FSF) and a specialized small target IoU loss function (STIoU) to refine detection precision. Simultaneously, the lightweight coarse-to-fine processing unit (LCF) and SCDown downsampling (SCD) form a comprehensive lightweight scheme, significantly reducing computational overhead while maintaining high detection efficacy. Extensive experiments were conducted on DUT-Plus, an augmented version of the DUT Anti-UAV dataset enhanced through data augmentation. The experimental results confirm that QHNet attains outstanding performance, providing an advanced balance between detection accuracy and computational efficiency across all model sizes.

Topics & Concepts