Phase Fringe Suppression of Background Deformation for Nondestructive Testing Based on Shearography

Abstract

Shearography, a commonly utilized method for nondestructive testing (NDT), offers the advantage of full-field, noncontact, and real-time inspection across extensive viewing areas. Nonetheless, the effectiveness of defect detection using this method is frequently compromised by background deformation, posing challenges in accurately determining defect sizes and locations. This study investigates the deformation observed in specimens during shearography defect detection and introduces a background fringe suppression strategy based on deep learning. The aforementioned approach, which incorporates a robust dataset generation methodology and an advanced network architecture, excels at processing noisy wrapped phase maps, thereby efficiently discerning and mitigating background fringes. The effectiveness of the proposed method is corroborated through simulated and actual defect detection experiments, underscoring its potential in enhancing the precision of shearography defect detection.