Algorithms for infrared image processing

In the present work, an algorithm for the analysis of raw thermal infrared images is proposed and exploited in two different applications: a nondestructive evaluation test and a tensile test. In the first one, the images are obtained by using the laser spot thermography and aim at detecting the presence of surface defects. A laser is used to scan a test specimen through the generation of single pulses. The temperature distribution produced by this thermoelastic source is measured by an infrared camera and processed with a two-stage algorithm. In the first stage, simple mathematical and statistical parameters are used to flag of the presence of damage. Then, once damage is detected, the thermal image’s first and second spatial derivative and two spatial filters are computed to enhance contrast, and to locate and size the defect. Some of the advantages of the proposed method with respect to existing approaches include automation in the defect detection process and better defective area isolation through increased contrast. The algorithm is first proven by analyzing simulated thermal images and then it is experimentally validated by scanning the surface of a CFRP composite plate with induced defects. In the second application, the same algorithm is applied to infrared images recorded during tensile tests on steel samples. Calorific manifestations accompany the elastoplastic transformation during tests. The algorithm, once more, is able to estimate automatically the state of damage of the specimen and to highlight the evolution of the deformation.