<ul><li>Traditional surface defect detection methods in industrial settings rely on manual inspection, which is inefficient and costly.</li><li>Automated defect detection approaches using Convolutional Neural Networks have limitations due to data annotation uncertainties and overfitting issues.</li><li>A statistically rigorous threshold is derived to identify high-probability defective pixels in test images, ensuring reliable detection.</li><li>The study demonstrates control over the expected test set error rate, validating the method's adaptability and effectiveness.</li></ul>

Conformal Segmentation in Industrial Surface Defect Detection with Statistical Guarantees

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