For each image, the CNN extracted 1000 features, which were subsequently used to train a SVM classifier. This approach achieved a maximum recognition accuracy of 98% for classifying CFRP hole delamination as either within or outside of tolerance from raw images. In addition to classification, deep learning can also be used for defect region segmentation. Hrechuk et al. utilized a CNN architecture (U-net) to identify defect regions from drill-hole images. 250 masked images with pre-identified defects, backgrounds and holes were used to train the U-net. As shown in Fig. (b), the predicted regions of defect showed good agreement with the ground truth image. In another study by Li et al. , a semi-supervised deep learning approach combining local exponential patterns (LEP) and U-net is proposed for automatic detection of the drilled-hole profile. The raw images were processed with LEP operator, ellipse fitting and semi-supervised labelling and the output masked images were deployed for training the U-net, see Fig. ). This method gives a good recognition accuracy with Intersection of Union index (IoU) > 0.9. As the lighting conditions and imagining angle can significantly affect the visibility and appearance of CFRP machining defects, Maghami et al. developed an automatic defects recognition method based on multi-light image and deep learning. The drilled hole is sequentially illuminated by 4 lights from different positions and the four images were fused to enhancing the contrast of the defects area, see Fig. ). Then U-net was trained and deployed to identify the three different areas (hole, delamination damage and crack in Fig. ). Delamination damage factor predicted with this method showed good agreement with raw images, with a maximum discrepancy of 5.4%.