Today the infrared thermography is among the nondestructive testing methods (NDT) most used for detection and characterization of internal defects in materials. It has become a reference method in industrial installations control. As the interpretation of thermal images provided by the infrared cameras is often difficult; therefore, it is necessary; to seek new methods fast and reliable for intelligent nondestructive evaluation. In our work we propose a fast method using artificial neural networks for internal defects depth evaluation from the thermal contrast. Experimental results have confirmed the method efficiency in predicting the defects depths.
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