Ultrasonic image stitching method for material defect detection

Shoupeng Song; Dan Chen; Yiqian Chen

doi:10.1117/12.2681149

8 June 2023 Ultrasonic image stitching method for material defect detection

Shoupeng Song, Dan Chen, Yiqian Chen

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 1270718 (2023) https://doi.org/10.1117/12.2681149
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Defect panoramic ultrasound image is helpful for material integrity evaluation in NDT&E. However, the single scan area of ultrasonic transducer is limited, which leading to the limitation of image area. Due to the low resolution and minimal detail in ultrasound images of defect, traditional visual image stitching methods cannot be directly applied to ultrasound defect images. Therefore, an ultrasonic image stitching method for material defect detection is proposed. According to the feature analysis of ultrasound defect images, an improved scale-invariant feature transform algorithm for ultrasound images stitching is developed. The algorithm first identifies image feature points in the same horizontal layer for coarse matching and then uses Euclidean distance histograms to eliminate mismatched pairs for fine matching. Thereafter, the algorithm transfers different images into the same coordinate system by using a transformation matrix obtained from a random sample consensus algorithm with an adaptive threshold. Finally, an improved fade -in/fade-out weighted fusion algorithm is used to stich the images. Acoustic field simulations and actual test experiments have been performed on an aluminum test sample to obtain a panoramic ultrasound defect image by using the proposed method.

Citation Download Citation

Shoupeng Song, Dan Chen, and Yiqian Chen "Ultrasonic image stitching method for material defect detection", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 1270718 (8 June 2023); https://doi.org/10.1117/12.2681149

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