# Comparing Object Detection Models for Electrical Substation Component Mapping

arXiv: https://arxiv.org/abs/2512.22454 · HTML: https://mlsanity.com/paper/2512.22454/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 85 references that are in the map.
- First posted on arXiv 27 Dec 2025.

## Builds on (most cited first, 26 of 26)

- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [DETRs Beat YOLOs on Real-time Object Detection](https://mlsanity.com/paper/2304.08069/) (2023)
- [RF-DETR: Neural Architecture Search for Real-Time Detection Transformers](https://mlsanity.com/paper/2511.09554/) (2025)
- [D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement](https://mlsanity.com/paper/2410.13842/) (2024)
- [Scale Match for Tiny Person Detection](https://mlsanity.com/paper/1912.10664/) (2019)
- [MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models](https://mlsanity.com/paper/2010.05352/) (2020)
- [LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection](https://mlsanity.com/paper/2406.03459/) (2024)
- [TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines](https://mlsanity.com/paper/2010.10032/) (2020)
- [Ultralytics YOLO Evolution: An Overview of YOLO27, YOLO26, YOLO11, YOLOv8, and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition](https://mlsanity.com/paper/2510.09653/) (2025)
- [InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV Images](https://mlsanity.com/paper/2311.01619/) (2023)
- [RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity](https://mlsanity.com/paper/2504.13099/) (2025)
- [Deep Learning in Automated Power Line Inspection: A Review](https://mlsanity.com/paper/2502.07826/) (2025)
- [Ensembles of Realistic Power Distribution Networks](https://mlsanity.com/paper/2208.06384/) (2022)
- [Quantifying cascading power outages during climate extremes considering renewable energy integration](https://mlsanity.com/paper/2407.01758/) (2024)
- [Comprehensive Performance Evaluation of YOLOv11, YOLOv10, YOLOv9, YOLOv8 and YOLOv5 on Object Detection of Power Equipment](https://mlsanity.com/paper/2411.18871/) (2024)
- [Deep Learning Based Solar Cell Recognition for Optical Wireless Power Transfer](https://mlsanity.com/paper/2410.14096/) (2024)
- [A Reproducible Method for Mapping Electricity Transmission Infrastructure for Space Weather Risk Assessment](https://mlsanity.com/paper/2412.17685/) (2024)
- [Lightweight G-YOLOv11: Advancing Efficient Fracture Detection in Pediatric Wrist X-rays](https://mlsanity.com/paper/2501.00647/) (2024)
- [Enhancing Power Grid Inspections with Machine Learning](https://mlsanity.com/paper/2502.13037/) (2025)
- [Container damage detection using advanced computer vision model Yolov12 vs Yolov11 vs RF-DETR A comparative analysis](https://mlsanity.com/paper/2506.22517/) (2025)
- [Small Dents, Big Impact: A Dataset and Deep Learning Approach for Vehicle Dent Detection](https://mlsanity.com/paper/2508.15431/) (2025)
- [RF-DETR for Robust Mitotic Figure Detection: A MIDOG 2025 Track 1 Approach](https://mlsanity.com/paper/2509.02599/) (2025)
- [A Modular Object Detection System for Humanoid Robots Using YOLO](https://mlsanity.com/paper/2510.13625/) (2025)

## Abstract (arXiv)

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.