用边缘云协同实现无人机实时检测干扰天线，延迟降88.9%。
Real-Time AIoT for AAV Antenna Interference Detection via Edge-Cloud Collaboration
- 采用关键帧选择与端到端定位结合的边缘云协同架构
- 自建数据集上检测准确率mAP达42.1%，参数仅300万
- 支持多无人机协同巡检，部署于Jetson与树莓派均实现实时推理
在5G时代，消除通信干扰源对维持网络性能至关重要。干扰常源于非法或故障天线，无线电监测机构每年需处理大量此类源。无人机可提升巡检效率，但现有纯云端人工智能模式的数据传输延迟无法满足实时性要求。为此，我们提出一种基于计算机视觉的物联网智能系统，用于无人机检测天线干扰源。系统采用优化的边缘云协同（ECC+）模式，结合关键帧选择算法（KSA），显著降低端到端延迟（E2EL）并保障可靠传输，契合超可靠低时延通信（URLLC）核心原则。核心为基于跟踪-检测范式的端到端天线定位方案，包含检测器（EdgeAnt）与追踪器（AntSort）。EdgeAnt在自建天线干扰源数据集上达到42.1% mAP，仅需300万参数和14.7 GFLOPs；在COCO数据集上，实现38.9% mAP，计算量5.4 GFLOPs。该模型部署于Jetson Xavier NX（TRT）和树莓派4B（NCNN），分别实现21.1（1088）和4.8（640）帧/秒的实时推理速度。相比纯云端模式，ECC+将端到端延迟降低88.9%，准确率提升28.2%。系统还具备优异的多无人机协同扩展能力。检测器代码已公开：https://github.com/SCNU-RISLAB/EdgeAnt。
原文摘要 · Abstract (English)
In the fifth-generation (5G) era, eliminating communication interference sources is crucial for maintaining network performance. Interference often originates from unauthorized or malfunctioning antennas, and radio monitoring agencies must address numerous sources of such antennas annually. Unmanned aerial vehicles (UAVs) can improve inspection efficiency. However, the data transmission delay in the existing cloud-only (CO) artificial intelligence (AI) mode fails to meet the low latency requirements for real-time performance. Therefore, we propose a computer vision-based AI of Things (AIoT) system to detect antenna interference sources for UAVs. The system adopts an optimized edge-cloud collaboration (ECC+) mode, combining a keyframe selection algorithm (KSA), focusing on reducing end-to-end latency (E2EL) and ensuring reliable data transmission, which aligns with the core principles of ultra-reliable low-latency communication (URLLC). At the core of our approach is an end-to-end antenna localization scheme based on the tracking-by-detection (TBD) paradigm, including a detector (EdgeAnt) and a tracker (AntSort). EdgeAnt achieves state-of-the-art (SOTA) performance with a mean average precision (mAP) of 42.1% on our custom antenna interference source dataset, requiring only 3 million parameters and 14.7 GFLOPs. On the COCO dataset, EdgeAnt achieves 38.9% mAP with 5.4 GFLOPs. We deployed EdgeAnt on Jetson Xavier NX (TRT) and Raspberry Pi 4B (NCNN), achieving real-time inference speeds of 21.1 (1088) and 4.8 (640) frames per second (FPS), respectively. Compared with CO mode, the ECC+ mode reduces E2EL by 88.9%, increases accuracy by 28.2%. Additionally, the system offers excellent scalability for coordinated multiple UAVs inspections. The detector code is publicly available at https://github.com/SCNU-RISLAB/EdgeAnt.
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