Transmission Defects Localization Network: Towards Wrong Assembly in the Transmission Assembly Process
Bibliographic record
Abstract
Transmission valve component is an important part of automobile manufacture. The success of the assembly of transmission valve is directly related to driving safety of vehicles. While localizing transmission assembly defects is particularly important in assembly of transmission valve component. As an image processing problem, real-time assembly images of transmission valve component are adopted to determine whether the assembly is correct or wrong. Transmission valve component in these images have small, severe reflection, and sparse properties, which increases the difficulty of detection. Therefore, this paper proposes a transmission defects localization network based on Siamese network for improving the performance of assembly of transmission valve components. In our model, we establish an image similarity evaluation network with designed multi-scale features fusion approach. Furthermore, in order to reduce intra-class spacing by similar transmission valve part samples on evaluation action, an improved binary cross entropy and focal loss function is discovered for feature re-processing. Finally, experimental results on real-world transmission assembly dataset indicate that our proposed approach outperforms other compared methods.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".