标题： 基于特征的边缘智能在电力系统中实时检测和跟踪外来物体入侵 显示英文标题

标题： Real-Time Detection and Tracking of Foreign Object Intrusions in Power Systems via Feature-Based Edge Intelligence

摘要： 本文提出了一种新颖的三阶段框架，用于电力传输系统中的实时异物侵入（FOI）检测和跟踪。 该框架集成了：（1）一个YOLOv7分割模型，用于快速且稳健的目标定位，（2）一个基于ConvNeXt的特征提取器，通过三元组损失进行训练以生成具有区分性的嵌入，以及（3）一个基于特征的IoU跟踪器，在遮挡和运动情况下确保稳健的多目标跟踪。 为了实现可扩展的现场部署，该流程优化为在低成本边缘硬件上使用混合精度推理进行部署。 通过将之前未见过的对象的嵌入添加到参考数据库中，系统支持增量更新，而无需重新训练模型。 在真实世界监控和无人机视频数据集上的大量实验表明，该框架在各种FOI场景中表现出高精度和鲁棒性。 此外，在NVIDIA Jetson设备上的硬件基准测试证实了该框架在实际边缘应用中的实用性和可扩展性。 显示英文摘要

摘要： 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.