Defect Detection by Analyzing Thermal Infrared Images Covered with Shadows with a Hybrid Approach Driven by Local and Global Intensity Fitting Energy

Abstract

Defect detection using thermal infrared images is used in nondestructive evaluation and testing because it is easy to use. Thermal infrared images recorded the surface temperatures of the target with a thermal infrared camera. Image segmentation is a technique to group those pixels with similar surface temperatures to form thermal patterns. Defects can be identified by the segmented patterns having different surface temperatures in their neighborhoods. In this study, a hybrid approach combining fitting energy is proposed for describing the contamination illustrated in the recorded surface temperatures and regional constants averaging the surface temperatures of the segmented regions. The numerical implementation is completed by applying the level set functions on an iteration scheme. The initial level sets evolve till a convergence can be reached. The processed results demonstrate that the hybrid approach can be used for defect detection.

Keywords

• image segmentation
• fitting energy
• regional constant
• level sets