Fiber optic networks demand flawless connector end-face quality, as microscopic scratches or debris cause major performance losses and downtime. Traditional inspection methods struggle with accuracy, variability, and speed. AI-powered deep learning overcomes these limits by using convolutional neural networks to detect, classify, and segment defects with pixel-level precision. Trained on expert-labeled datasets, these models adapt to new defect types and outperform manual inspection in consistency and speed—scanning MT-12 connectors in under 4 seconds. Automated systems enhance compliance with IEC standards while offering scalability, retraining flexibility, and reproducibility for manufacturing and field use.

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