Which practices improve accuracy in automated segmentation and labeling of defects in VIM data?

• A. Use Low-Quality Ground Truth Data
• B. Use High-Quality Ground Truth Data, Diverse Defect Samples, Cross-Validation, and Clear Labeling Conventions
• C. Avoid Manual Review
• D. Use Random Labeling

Answer: (B)

Improving accuracy in automated segmentation and labeling of defects in VIM data comes from training the model on targets that truly reflect real defects and evaluating it in a way that mirrors real use. High-quality ground truth data provides precise, reliable boundaries and correct labels, so the model learns where a defect starts and ends and what type it is without being misled by noisy or incorrect targets. Including diverse defect samples ensures the model sees a wide variety of defect appearances, scales, textures, and imaging conditions, which helps it generalize beyond the specific examples in the training set rather than performing well only on a narrow subset. Cross-validation gives a robust sense of how the model will perform on new data by testing it across multiple splits, reducing the risk of overfitting to one particular partition. Clear labeling conventions remove ambiguity and inconsistency, producing consistent targets for the model to learn from and enabling more accurate and repeatable segmentation and labeling.

Low-quality ground truth, meanwhile, would teach the model incorrect boundaries and labels, hurting accuracy. Relying on no manual review can let annotation mistakes slip through, undermining quality. Random labeling provides no meaningful signal for learning and will fail to guide the model toward correct segmentation and identification.