Évaluation de l’efficacité des technologies de vision pour l’inspection des wagons de chemin de fer
Bibliographic record
Abstract
Railroads worldwide are leveraging machine vision technologies to enhance railcar inspection quality and efficiency, ultimately improving railway safety. In collaboration with Canadian Pacific Kansas City (CPKC), the University of Alberta (U of A), TC’s Rail Safety and Security Directorate, and the National Research Council Canada (NRC), Transport Canada’s Innovation Centre (TC) launched the Automated Machine Vision Inspection Systems (AMVIS) project in 2021 to assess the capabilities of remotely monitored train inspection technologies. This project studied the reliability of the Train Inspection Portal System (TIPS) under various climatic conditions, the effectiveness of Portal Office Inspection (POI) in detecting safety defects, and the potential of AI algorithms to support inspectors. The results provide evidence that TIPS enables real-time, high-quality imaging without disrupting train operations, reduce idling time and improve defect detection for several defect types. Additionally, AI models such as YOLOv5 and Faster R-CNN demonstrate strong potential in automating defect identification, particularly for wheels and cap screws. Key recommendations include optimizing camera placement for enhanced imaging, refining POI software for improved defect detection, and integrating AI-driven solutions to elevate inspection accuracy and efficiency.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".