Design of Infrared Thermal Wave Image Defect Detection System Based on LabVIEW

Carbon fiber reinforced composites are widely used in aviation field, which requires higher quality. But the traditional manual detection method has high work intensity and low efficiency. In order to improve the defect detection efficiency of carbon fiber reinforced plastics (CFRP), this study designed a defect detection system based on LabVIEW software development platform, extracted defect edges and performed quantitative statistics. In this study, active infrared thermal imaging non-destructive testing technology is used to obtain the surface thermal images of damaged samples scanned by laser through infrared thermal imager. In view of the poor contrast and uniformity of infrared images, HSL(Hue, Saturation, Luminance) is used to carry out color plane extraction and gray transform, and Niback local threshold segmentation algorithm suitable for processing images with uneven illumination distribution is selected to carry out threshold segmentation processing of image of the region of interest. Finally, morphological processing is used to enhance the image and realize defect feature extraction and defect number statistics. In this study, an infrared thermal imaging defect detection experiment platform is built to complete the acquisition and processing of infrared thermal wave defect images, and the software platform and user interface are designed to realize the extraction of the defect features. Compared with manual detection, the design of this system significantly reduces the detection time and is helpful in realizing the automation of defect detection.