Work

Intelligent Defect Identification in Girth Welds of Phased Array Ultrasonic Testing Images Using Median Filtering, Spatial Enrichment, and YOLOv8

Year: 2026

Type: article

Abstract: Girth welds are susceptible to defects under high internal pressure and stress. While phased array ultrasonic testing (PAUT) is widely used for non-destructive evaluation, manual inspection remains inefficient and highly dependent on expertise. Furthermore, existing deep learning models often struggle with low accuracy and high complexity. This paper proposes a PAUT defect classification method based on YOLOv8. First, median filtering is employed for denoising, and the results show that noise is effectively reduced while preserving key features, achieving PSNR values of 35.132, 35.938, and 36.138 for slag inclusion, pores, and lack of fusion (LOF), respectively. Subsequently, the spatial enrichment algorithm (SEA) is applied to enhance image details without amplifying noise, yielding a PSNR of 33.71 and an SSIM of 0.96. Finally, the YOLOv8 model is implemented for defect recognition. Experimental results demonstrate that the proposed approach achieves a superior balance between precision and recall with high reliability. This method offers a robust and efficient solution for automated PAUT evaluation in practical engineering applications.

Source: Metals

DOI: 10.3390/met16050458

Authors: Mingzhe Bu, Shengyuan Niu, Xueda Li, Bin Han

Institutions: China University of Petroleum, Beijing, China University of Petroleum, East China

Language: English

Topic: Non-Destructive Testing Techniques

Subfield: Mechanical Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Key Research and Development Program of China