Active Thermography and YOLOv8 for Subsurface Defect Detection

Over the last few years, non-destructive testing techniques and deep learning algorithms are widely adopted in the industry field, especially for material inspection. Deep learning-based object detection techniques has demonstrated tremendous potential in the material inspection field. The main objective of this study is to apply active thermography and deep learning-based object detection techniques to the field of material inspection. Thus, we investigate the effectiveness of the YOLOv8s and YOLOv8n in detecting subsurface defects in Carbon FiberReinforced Polymer (CFRP) material from thermal images.