A Deep Learning Approach for Automated Re-Inspection of False Calls in Mixed-Production Surface Mount Lines Under Extreme Class Imbalance

Automated Optical Inspection equipment on Surface Mount Technology lines is tuned conservatively so that genuine defects are not missed, producing a persistent stream of false calls that a human operator must reconfirm in a secondary re-inspection step. This study reports a deep learning classifier for that decision, developed and evaluated on 34,466 images from one operating mixed-model line with an acceptable-to-defective ratio of 196 to 1. A three-stage procedure screens five architectures, compares three augmentation intensities, and sweeps twenty class weight settings under five-fold cross-validation. Two operating points are reported rather than one best model: a balanced configuration at 84.85% precision and 78.29% recall, and a high-capture configuration at 89.14% recall. Restricting evaluation to boards absent from the training partition gives 73.69% and 87.71% recall, indicating that the image-level split inflates recall by one to five percentage points. Because the classifier filters a population currently reviewed in full by hand, both configurations reduce human review volume by more than 98% on this line. An illustrative cost comparison, conditioned on an assumed accuracy of the manual re-inspection it replaces, indicates which operating point is preferred at a given ratio of released-defect cost to review cost. A secondary observation, offered as a hypothesis rather than a demonstrated result, is that per-model learning rates fixed before an augmentation study may produce apparent architecture-specific failures that are optimization artifacts.