APLICAÇÃO DE YOLOv8 E MARCADORES ARUCO PARA RECONHECIMENTO E MEDIÇÃO DE PARAFUSOS.
Bibliographic record
Abstract
RESUMO: Este estudo aborda a integrao do YOLOv8 e Marcadores ArUco para aprimorar a deteco e medio de parafusos.O objetivo principal desenvolver uma soluo que ir permitir a identificao e a medio de parafusos com preciso utilizando viso computacional.A metodologia envolve a utilizao de uma webcam para capturar imagens, onde o algoritmo YOLO realiza a deteco dos objetos ,e os Marcadores ArUco so empregados para calcular as dimenses dos parafusos.O sistema foi implementado para capturar e salvar imagens dos objetos, alm de registrar as medidas em uma planilha.A pesquisa foi realizada em ambiente controlado, com foco na integrao dos sistemas.Os resultados demonstraram a eficincia do sistema em identificar e medir parafusos de forma automatizada e precisa mostrando que a combinao de tcnicas de viso computacional pode aumentar a capacidade de sistemas robticos em tarefas de manipulao e inspeo de objetos, sendo vivel em processos de inspeo industrial e controle de qualidade.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".