Defect detection in thermal image for nondestructive evaluation of petrochemical equipments

This paper proposes a method for segmenting defects depicted in a thermal image of petrochemical equipments by means of passive thermography. The technique first enhances the contrast of the defects based on local neighborhood pixel intensity operation. This local intensity operation works in two modes, either brightening the pixels for detecting hot spots or darkening the pixels for detecting cold spots. The next step is to segment the defects using simple histogram-based thresholding techniques. We propose three thresholding methods: mean absolute thresholding (MAT), mean relative thresholding (MRT), and minimum frequency thresholding (MFT). Compared to existing techniques, we found that our proposed methods have better detection and success rate.