Dual-Scale Dataset-Based Intelligent Recognition of Power Equipment for Enhanced Digital Grid Planning
Bibliographic record
Abstract
In the evolution of digital power grids, technologies such as artificial intelligence and digital twins have emerged as foundational elements.A critical step in this evolution involves creating digital avatars of physical power equipment, enabling precise classification crucial for digital twin integration.Traditional algorithms for equipment recognition face significant challenges due to discrepancies in equipment sizes, and environmental factors like bright light and haze, which substantially degrade detection performance.This study introduces a novel single-stage recognition method that employs staged training on a dual-scale dataset, specifically designed to address these challenges.Equipment images are categorized into large-scale and small-scale sets to mitigate issues arising from size disparities.Furthermore, a comprehensive dataset featuring multiple scales, angles, and lighting conditions is compiled, enhancing the model's generalizability and robustness.The proposed method incorporates a feature extraction module, a feature fusion network, and environment context modeling, which are trained separately on the large-scale and small-scale datasets.During testing, outputs from the dual-scale models are integrated.Comparative experiments demonstrate that the proposed method requires only one-third the parameters of the SSD algorithm, yet operates at a detection speed of 52.15 frames per second (fps).Despite its lightweight structure, the algorithm achieves an impressive mean Average Precision (mAP) of 92.13%, effectively reducing false and missed detections.This performance signifies a marked improvement in stability and robustness over existing single-stage detection methods, particularly in complex natural environments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".