The detection of cracks on metallic parts is critical in many industrial applications. Thermography is one of the most effective methods for this purpose. In laser heating the infrared images are strongly affected by laser reflection or secondary emission. To avoid this problem, during the measurement, the laser beam can be periodically alternated between on and off states. For this technique to be effective, it is important to take into account the phase of the laser (e.g. on/off state) at each image frame. Due to the noisy and non-stationary nature of the observed real and apparent temperature of the surface at the location of the laser spot, it is challenging to detect the phase of laser pulse. In this work, Bayesian recursive filtering (BRF) is successfully applied for achieving this goal. This method can seamlessly account for non-periodic conditions and inaccuracies in laser modulation frequency and sampling rate.

Date Published: 16 July 2019
PDF: 7 pages
Proc. SPIE 11172, Fourteenth International Conference on Quality Control by Artificial Vision, 111720E (16 July 2019); doi: 10.1117/12.2521737

Published in SPIE Proceedings Vol. 11172:
Fourteenth International Conference on Quality Control by Artificial Vision
Christophe Cudel; Stéphane Bazeille; Nicolas Verrier, Editor(s)