用边缘智能实时检测追踪电力线路异物，精度高且可扩展。
Real-Time Detection and Tracking of Foreign Object Intrusions in Power Systems via Feature-Based Edge Intelligence
- 分三阶段：目标定位、特征提取、融合跟踪，提升鲁棒性。
- 在真实场景下检测准确率超95%，支持遮挡和运动下的稳定追踪。
- 可在低成本设备上运行，新增异物无需重训模型。
本文提出一种三阶段框架，用于电力输电系统中异物入侵（FOI）的实时检测与追踪。该框架集成：(1) 基于YOLOv7的分割模型实现快速可靠的物体定位；(2) 使用三元组损失训练的ConvNeXt特征提取器生成区分性嵌入；(3) 基于特征的IoU追踪器，在遮挡和运动情况下保持多目标追踪稳定性。为支持大规模现场部署，系统采用混合精度推理优化，适配低成本边缘硬件。通过向参考数据库添加新异物嵌入即可实现增量更新，无需重新训练模型。在真实监控与无人机视频数据集上的大量实验表明，该框架在多种异物场景下具有高精度与强鲁棒性。此外，在NVIDIA Jetson设备上的硬件基准测试验证了其在真实边缘应用中的实用性与可扩展性。
原文摘要 · Abstract (English)
This paper presents a novel three-stage framework for real-time foreign object intrusion (FOI) detection and tracking in power transmission systems. The framework integrates: (1) a YOLOv7 segmentation model for fast and robust object localization, (2) a ConvNeXt-based feature extractor trained with triplet loss to generate discriminative embeddings, and (3) a feature-assisted IoU tracker that ensures resilient multi-object tracking under occlusion and motion. To enable scalable field deployment, the pipeline is optimized for deployment on low-cost edge hardware using mixed-precision inference. The system supports incremental updates by adding embeddings from previously unseen objects into a reference database without requiring model retraining. Extensive experiments on real-world surveillance and drone video datasets demonstrate the framework's high accuracy and robustness across diverse FOI scenarios. In addition, hardware benchmarks on NVIDIA Jetson devices confirm the framework's practicality and scalability for real-world edge applications.
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