A Safety Detection Model for Substation Operations Based on Combination of Spatial Context Reasoning Algorithm and Deep Learning Techniques
Bibliographic record
Abstract
The environment near substations is complex, and electrocution accidents of operators occur from time to time during on-site operations, and the development of safety detection models for substation operations has received more and more attention.The article proposes a safety distance detection model for substation operation, which is mainly composed of binocular stereo matching perception model and safe area detection model.The binocular stereo matching perception is based on the PSMNet network model, combined with the parallax regression calculation to obtain the threedimensional coordinates of the operation area in the process of substation operation, and the threedimensional reconstruction of the substation operation process.The spatial context inference algorithm is utilized in the safe region detection model to detect the edge of the safe region, and the image segmentation of the safe region of the substation operation scene is performed by the improved OTSU algorithm.Then the three-dimensional coordinates obtained from binocular stereo matching perception and the three-dimensional coordinates of safe region detection are solved for the Euclidean distance, and then the safe distance detection of substation operation is realized.The EPE result accuracy of binocular stereo perception matching on the dataset is reduced by 0.71px compared with CRL, and the resulting mismatch pixel rate is between 0.83 and 1.48%.The average time-consuming image segmentation of the improved OTSU threshold segmentation method is 6.34ms, and the average relative error of the safety distance detection for substation operation is only 0.85%, and the maximum absolute error of the safety distance detection is only 0.13 m.Combining the spatial contextual reasoning algorithm with the deep learning technology can realize the effective detection of the safety distance for substation operation in multiple scenarios, and fully ensure the operation of the substation workers' safety.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".