Comparing Object Detection Models for Electrical Substation Component Mapping

TL;DR

This paper compares three computer vision models—YOLOv8, YOLOv11, and RF-DETR—for automating the detection of electrical substation components to improve mapping efficiency and accuracy, aiding infrastructure vulnerability assessment.

Contribution

It evaluates and contrasts the performance of three models on substation images, identifying the most reliable for large-scale component mapping in the US.

Findings

RF-DETR shows higher detection accuracy.

YOLOv11 offers a good balance of speed and precision.

Models enable efficient large-scale substation mapping.

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.