To methods (such as C-Scan, X-ray, and thermography) are available for detecting machining-induced defects, vision-based inspection has become the most widely used approach as it offers a fast and cost-effective solution for industrial applications . In vision-based inspection, image processing techniques are required to detect and recognize regions of defects (delamination, burr, cracks etc.) and distinguish them from natural surface pattern of CFRP. Despite several classical image analysis algorithms have been deployed for this purpose, they often require extensive manual calibration and can be influenced by lighting conditions. To address this issue, data-driven deep learning-based image recognition methods have been developed to achieve accurate and efficient recognition of machining-induced defects directly from raw captured images.