The defect feature extraction of ultrasonic phased array detection based on adaptive region growth

Abstract

An ultrasonic phased array defect extraction method based on adaptive region growth is proposed, aiming at problems such as difficulty in defect identification and extraction caused by noise interference and complex structure of the detected object during ultrasonic phased array detection. First, bilateral filtering and grayscale processing techniques are employed for the purpose of noise reduction and initial data processing. Following this, the maximum sound pressure within the designated focusing region serves as the seed point. An adaptive region iteration method is subsequently employed to execute automatic threshold capture and region growth. In addition, mathematical morphology is applied to extract the processed defect features. In the final stage, two sets of B-scan images depicting hole defects of varying sizes are utilized for experimental validation of the proposed algorithm's effectiveness and applicability. The defect features extracted through this algorithm are then compared and analyzed alongside the histogram threshold method, Otsu method, K-means clustering algorithm, and a modified iterative method. The results reveal that the margin of error between the measured results and the actual defect sizes is less than 13%, representing a significant enhancement in the precision of defect feature extraction. Consequently, this method establishes a dependable foundation of data for subsequent tasks, such as defect localization and quantitative and qualitative analysis.

Fig 1. Defect feature extraction block diagram.

Fig 2. Schematic diagram of region growth in the B-scan image.

Fig 3. Actual diagram of the B-type test block detection.

Fig 4. Physical drawing of the type B test block and original detection image.

Fig 5. Grayscale image.

Fig 6. Detected image after bilateral filtering.

Fig 7. The a-wave amplitude at the scan line.

Fig 8. Schematic diagram of initial seed point selection.

Fig 9. Pseudocode of the adaptive region growing algorithm in this paper.

Fig 10. Mathematical morphological processing.

(a) The binarized image after segmentation; (b) The image after the closed operation; (c) The image marked by the Sobel operator.

Fig 11. 1# detection images processed by different methods.

(a) Original image; (b) The proposed algorithm; (c) Histogram threshold method; (d) Otsu-AWDO method; (e) K-means clustering algorithm; (f) Modified iterative method.

Fig 12. 2# detection images processed by different methods.

(a) Original image; (b) proposed algorithm; (c) histogram threshold method; (d) Otsu-AWDO method; (e) K-means clustering algorithm; (f) modified iterative method.