Deep learning systems are increasingly used in manufacturing to handle variations in part appearance, where traditional rule-based machine vision struggles. While rule-based systems perform well with consistent features, deep learning excels at identifying defects and inconsistencies by being trained on labeled images of both good and defective parts. Validating a deep learning system in manufacturing requires accurate ground truth data and statistically significant training datasets to ensure reliable performance on the production line. This approach enables more accurate inspections and greater adaptability in unpredictable environments.

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