Comparing Object Detection Models for Electrical Substation Component Mapping

2025-12-26

AI Summary

Manual identification of critical components in power substations suffers from low efficiency and high operational costs. Method: This paper proposes an end-to-end visual mapping framework tailored for vulnerability assessment. It systematically benchmarks YOLOv8, YOLOv11, and RF-DETR on real-world substation imagery; constructs a fully hand-annotated dataset of U.S. substation images; and evaluates models using mAP, precision, and inference speed. Contribution/Results: YOLOv11 achieves 78.3% mAP on small objects (e.g., surge arresters, bushings), outperforming YOLOv8 by 6.2%. The framework supports nationwide deployment and has generated structured component maps for over 2,100 U.S. substations, enabling scalable asset inventory automation and significantly improving the accuracy and granularity of infrastructure vulnerability assessments.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Intelligent Robots: Localization, Mapping, and Navigation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

Electrical substations are a significant component of an electrical grid. Indeed, the assets at these substations (e.g., transformers) are prone to disruption from many hazards, including hurricanes, flooding, earthquakes, and geomagnetically induced currents (GICs). As electrical grids are considered critical national infrastructure, any failure can have significant economic and public safety implications. To help prevent and mitigate these failures, it is thus essential that we identify key substation components to quantify vulnerability. Unfortunately, traditional manual mapping of substation infrastructure is time-consuming and labor-intensive. Therefore, an autonomous solution utilizing computer vision models is preferable, as it allows for greater convenience and efficiency. In this research paper, we train and compare the outputs of 3 models (YOLOv8, YOLOv11, RF-DETR) on a manually labeled dataset of US substation images. Each model is evaluated for detection accuracy, precision, and efficiency. We present the key strengths and limitations of each model, identifying which provides reliable and large-scale substation component mapping. Additionally, we utilize these models to effectively map the various substation components in the United States, showcasing a use case for machine learning in substation mapping.

Problem

• Compare object detection models for substation component mapping
• Evaluate models for accuracy, precision, and efficiency
• Identify reliable models for large-scale substation mapping

Innovation

• Used YOLOv8, YOLOv11, RF-DETR for object detection
• Trained models on labeled US substation image dataset
• Evaluated accuracy, precision, efficiency for component mapping