Automated Defect Detection Using Threshold Value Classification Based on Thermographic Inspection
Seungju Lee, Yoonjae Chung, Ranjit Shrestha, Wontae Kim
Abstract
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.