<p>The early detection of internal defects in wind turbine blades made of composite materials is crucial to prevent potential damage that could compromise their structural integrity and performance. Even minor defects, like porosity or fiber misalignment, can significantly affect the blade’s durability and strength, leading to costly maintenance or replacement. This study examines four common types of sub-surface defects in composite material specimens, such as fiber misalignment, inclusion, fiber breakage, and porosity. We utilize a series of thermal images obtained through long-pulse active thermography of samples with induced sub-surface defects. Our approach analyzes the thermal image sequence to capture temperature changes after heat pulse exposure. In this work, we present a segmentation method that effectively identifies regions corresponding to sub-surface defects based on k-means clustering and watershed transform. Furthermore, our approach utilizes principal component projections for the visual identification of sub-surface defects. This valuable data is then fed into a set of machine learning classifiers, enabling their automatic classification. Remarkably, the naïve Bayes classifier excelled in accurately identifying the four distinct types of sub-surface defects in the specimens. By integrating the proposed segmentation and classification methods, our study enables the comprehensive analysis of sub-surface defects in composite materials, providing significant advancements for quality control in the manufacturing of wind turbine blades. Additionally, our study demonstrates the reliability of the active long-pulse thermography data for identifying and classifying sub-surface defects in wind turbine blades.</p>