Automatic and Accurate Determination of Defect Size in Shearography Using U-Net Deep Learning Network

Shearography, an effective non-destructive testing tool, is widely employed for detecting defects in composite materials. It detects internal defects by detecting deformation anomalies, offering advantages such as full-field, non-contact measurement, and high accuracy. Defect size is a critical parameter determining structure performance stability and service life. However, manual inspection is the primary method for defect size measurement in this technique, leading to inefficiency and low accuracy. To address this issue, this study established a defect recognition and high-precision automatic measurement method based on the U-Net deep learning network. First, a high-precision one-time calibration method for all system parameters was developed. Second, U-Net was employed to segment the measured image, identifying defect location and subimage. Finally, defect size was accurately calculated by combining calibration parameters and segmented defect subimage. The proposed method yielded a measurement error of less than 5% and a real-time dynamic detection rate of 14 fps, demonstrating potential for automated quantitative defect detection.