Song Yongfeng, Turner Joseph A, Peng Zuoxiang, Chao Chen, Li Xiongbing
IEEE Trans Ultrason Ferroelectr Freq Control. 2018 Apr 17. doi: 10.1109/TUFFC.2018.2827464.
In an attempt to improve the ultrasonic testing capability of a conventional C-scan system, a flaw detection method using an ultra-high gain is developed in this paper. A time-dependent threshold for image segmentation is applied to identify automatically material anomalies present in the sample. A singly-scattered response (SSR) model is used with extreme value statistics to calculate the confidence bounds of grain noise. The result is a time-dependent threshold associated with the grain noise that can be used for segmentation. Ultrasonic imaging experiments show that the presented method has advantages over a traditional fixed threshold approach with respect to false positives and missed flaws. The results also show that a low gain is adverse to the detection of micro-flaws with subwavelength dimensions. The forward model is expected to serve as an effective tool for the probability of detection (POD) of flaws and the inspection of coarse-grained materials in the future.
为了提高传统C扫描系统的超声检测能力，本文开发了一种使用超高增益的探伤方法。应用随时间变化的阈值进行图像分割，以自动识别样品中存在的材料异常。采用单散射响应（SSR）模型和极值统计来计算颗粒噪声的置信区间。结果是得到一个与颗粒噪声相关的随时间变化的阈值，可用于分割。超声成像实验表明，该方法在误报和漏检方面优于传统的固定阈值方法。结果还表明，低增益不利于检测亚波长尺寸的微缺陷。该正向模型有望在未来成为缺陷检测概率（POD）和粗晶材料检测的有效工具。
