- 期刊
- OpenAccess
Statistical Thresholding of Ultrasonic Amplitude Maps for Automated Damage Segmentation
摘要
Amplitude maps generated by ultrasound imaging are frequently utilized to visualize invisible damages in thin-walled aero-mechanical structures. Accurate evaluation of damage size from these maps is crucial; however, a reliable automated method for this purpose under the influence of imaging noise is not available. To address this issue, four threshold calculation methods based on statistical analysis of noise content in an amplitude map were developed. These candidates were numerically optimized using amplitude maps containing damages of various sizes and Gaussian noise of differing intensities. The candidate demonstrating the greatest immunity to parameter variations and the highest potential for accurate damage size evaluation was identified. This candidate was then parametrically optimized and benchmarked against k-means clustering. The results demonstrate that the newly proposed statistical thresholding method outperformed k-means clustering, consistently providing high accuracy above 99% even for damages as small as 2 mm and signal-to-noise ratios (SNR) as low as 3 dB.