Inspection Detectability Improvement for Metal Defects Detected by Pulsed Infrared Thermography

Abstract

Aiming at the drawbacks of low contrast and high noise in the thermal images, a novel method based on the combination of the thermal image sequence reconstruction and the first-order differential processing is proposed in this work, which is comprised of the following procedures. Firstly, the specimen with four fabricated defects with different sizes is detected by using pulsed infrared thermography. Then, a piecewise fitting based method is proposed to reconstruct the thermal image sequence to compress the data and remove the temporal noise of each pixel in the thermal image. Finally, the first-order differential processing based method is proposed to enhance the contrast. An experimental investigation into the specimen containing de-bond defects between the steel and the heat insulation layer is carried out to validate the effectiveness of the proposed method via the above procedures. The obtained results show that the proposed method can remove the noise, enhance the contrast, and even compress the data reaching at 99.1%, thus improving the detectability of pulsed infrared thermography on metal defects.

Application of the Machine Vision Technology and Infrared Thermography to the Detection of Hoof Diseases in Dairy Cows: A Review

“…A common operation leading to image enhancement is noise reduction, in which, for example, a Gaussian filter is usually applied, which belongs to the group of linear filters and reduces noise very effectively by smoothing the image at the expense of fine details [66,78]. The main representative of nonlinear filtering techniques is the median filter [94], which replaces the value of each point of the original image with the median of the values of the points that surround it, thus eliminating isolated noise points [103]. The median filter has a very strong effect on suppressing salt and pepper noise [85,104].…”