arXiv Computer Vision October 9, 2026 By Namish Bansal (George Mason University), Haley Mody (George Mason University), Dennies Kiprono Bor (George Mason University), Dante Groccia (George Mason University), Edward J. Oughton (George Mason University)

Comparing Object Detection Models for Electrical Substation Component Mapping

Read the original on arXiv Computer Vision →

The paper evaluates 16 computer‑vision models for mapping electrical substation components, training them on a manually labeled dataset of U.S. substation images. The best performer, RF‑DETR‑large, achieved mAP@50 of 0.881 and mAP@50:95 of 0.632, with alternate energy systems detected most accurately and transformers/reactors posing the greatest challenge. Applying this model nationwide produced about 22,591 component detections across 11,083 substations, with Florida and the MISO region showing the highest counts.

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