Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection

Active infrared thermography is an attractive and reliable technique used for the non-destructive evaluation of various materials and structures, because it enables non-contact, large area, high-speed, quantitative, and qualitative inspection. However, the defect detectability is significantly deteriorated due to the excitation of a non-uniform heat source and surrounding environmental noise, requiring additional signal processing and image characterization. The lock-in infrared thermography technique has been proven to be an effective method for quantitative evaluation by extracting amplitude and phase images from a 2D thermal sequence, but it still involves a lot of noise, providing difficulties in detection. Therefore, this study explored the possibility of improving the signal-to-noise ratio by applying filtering to a stainless-steel plate with circular defects. Thereafter, automated defect detection was performed based on the threshold value through the binary images. In addition, a comparative analysis was performed to evaluate the detectability according to the presence or absence of a filtering application.