Real-Time Detection and Tracking of Foreign Object Intrusions in Power Systems via Feature-Based Edge Intelligence
This addresses the problem of foreign object intrusions in power systems for utility operators, offering a scalable edge solution with incremental updates, though it is incremental as it combines existing methods.
The paper tackles real-time detection and tracking of foreign object intrusions in power transmission systems by proposing a three-stage framework integrating YOLOv7 segmentation, ConvNeXt-based feature extraction, and feature-assisted IoU tracking, achieving high accuracy and robustness across diverse scenarios with deployment on low-cost edge hardware.
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.