Electric Power Fuse Identification with Deep Learning
Bibliographic record
Abstract
Paper Title: Electric Power Fuse Identification with Deep Learning Journal Title: IEEE Transactions on Industrial Informatics Accepted On: February 6, 2023 GitHub Link: https://github.com/MEDomics-UdeS/energAI-fuses Description: This project implements a supervised learning PyTorch-based end-to-end object detection pipeline for the purpose of detecting and classifying fuses in low-voltage electrical installations. Files: annotations.csv: Contains bounding box annotations for images. Save the file to 'data/annotations/' in the cloned GitHub repository. final_model.pkl: Final trained model. Save the file anywhere and select it using the GUI from the cloned GitHub repository to use it. images.zip: Images for learning and testing. Extract the .zip file to 'data/raw/' in the cloned GitHub repository. Abstract: As part of arc flash studies, survey pictures of electrical installations need to be manually analyzed. A challenging task is to identify fuse types, which can be determined from physical characteristics such as shape, color and size. To automate this process using deep learning techniques, a new dataset of fuse pictures from past arc flash projects and data from the web was created. Multiple experiments were performed to train a final model, reaching an average precision (AP50) of 91.06 % on the holdout set, which confirms its potential for identification of fuse types in new photos. By identifying fuse types using physical characteristics only, the need to take clear pictures of the label text is eliminated, allowing pictures to be taken away from danger, thereby improving the safety of workers. All the resources needed to repeat the experiments are openly accessible, including the code and datasets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.010 | 0.010 |
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".