Comparing Object Detection Models for Electrical Substation Component Mapping

Namish Bansal; Haley Mody; Dennies Kiprono Bor; Dante Groccia; Edward J. Oughton

cs.CVDec 27, 2025

Comparing Object Detection Models for Electrical Substation Component Mapping

Authors: Namish Bansal, Haley Mody, Dennies Kiprono Bor, Dante Groccia, Edward J. Oughton

Organizations: George Mason University

Abstract

Electrical substations are a significant component of an electrical grid. Indeed, the assets at these substations (e.g., transformers) are vulnerable to hazards such as hurricanes, flooding, earthquakes, and geomagnetically induced currents (GICs). Because failures can have significant economic and public safety implications, identifying key substation components is essential for quantifying 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 offers greater convenience and efficiency. In this study, we train and compare 16 models on a manually labeled dataset of US substation images. These models include 12 You Only Look Once (YOLO) models, 2 Roboflow Detection Transformer (RF-DETR) models, and 2 Cascade R-CNN models. RF-DETR-large achieved the highest overall detection performance with mAP@50 and mAP@50:95 scores of 0.881 and 0.632, respectively. Across all models, alternate energy systems were detected most accurately, while transformers and reactors were more difficult to identify due to their smaller size and greater visual variability. Applying our best-performing model to nationwide imagery yielded approximately 22,591 component detections across 11,083 unique substations within the United States. These detections were broken down by state and Federal Energy Regulatory Commission (FERC) regions, with Florida (2,478 detections) and Midcontinent Independent System Operator (MISO; 4,329 detections) having the largest number of detections in their respective categories.

Figures & tables

Figure 1: Annotation samples of different substation components.

Figure 2: This figure showcases each of the steps used in this section, from model preparation and data augmentation to running inference on substation imagery.

Figure 3: The results of our model training. Panels (a) and (b) show overall model performance in terms of mAP@50 and mAP@50:95; panels (c)–(f) show per-class AP50. Alt Energy denotes alternate energy systems. Error bars represent 95% confidence intervals calculated using 1,000 bootstrap resamples.

Figure 4: Detected components per sampled image: (a) all 11,085 images, with means in parentheses; (b) the 11,083 geographically assigned images, with regional sample sizes. Boxes show interquartile ranges and medians; whiskers extend to observations within 1.5 interquartile ranges. Points represent images and diamonds indicate means. Zero-detection images are retained; Overall sums the four retained classes, excluding Control and Power Lines. The horizontal scale changes beyond 10 detections. NGC denotes NorthernGridConnected, NGU NorthernGridUnconnected, WCNE WestConnectNonEnrolled, and WC WestConnect.

Figure 5: Component detections by state (top ten; a) and FERC Order 1000 planning region (b), compared with state population density (c) and regional sampled image count (d). Panel (c) includes 47 sampled states with supplied population values; DC is excluded. Colour denotes detections per million people in (c) and mean detections per sampled image in (d). Marker size denotes total detections; shapes in (c) identify broad U.S. regions. Panel (d) describes sample coverage and does not estimate regional population density.

Figure 6: Choropleth maps showing component detections by planning region (a) and state/DC (b), and detections per 100 km 2 by planning region (c) and state/DC (d). Both geographic summaries include 11,083 images and 22,591 detections. State densities use Census land area; regional densities use dissolved polygon area in EPSG:5070. Hatching indicates no sampled images.

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