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Academic Journal of Computing & Information Science, 2024, 7(4); doi: 10.25236/AJCIS.2024.070416.

Enhanced Image Segmentation-Based Detection Technique for X-ray Film Images of Weld Seams

Author(s)

Ruixiang Li1, Shanwen Zhang1, Lei Huang2, Mingda Yang1, Chengyu Hu1

Corresponding Author:
Shanwen Zhang
Affiliation(s)

1School of Electronic Information, Xijing University, Xi'an, China

2Tubular Goods Research Institute of CNPC, Xi’an, 710077, China

Abstract

In industrial pipeline systems, quality monitoring of steel pipes and welds is a critical component to ensure safe operation. Utilizing deep learning techniques to analyze X-ray images can efficiently identify weld defects, such as porosity, cracks, and inclusions, significantly enhancing the accuracy and efficiency of non-destructive testing. In response to the existing weld defect detection models' insufficient feature extraction and lack of handling diversity, this paper introduces a weld defect identification method based on the Hierarchical Attention Fusion Network (HAFNet). Initially, a Dilated Hierarchical Attention Mechanism (DHAM) is employed to capture multi-scale global and local information, thereby enhancing the focus on key features of different scales and effectively addressing the issue of large intra-class variability and small inter-class differences in defects. Subsequently, a Residual Fusion Module (RFM) is introduced, which adaptively learns the feature weights of different encoding layers and fully utilizes contextual information during the decoding phase to suppress the complex background interference of weld images. Finally, through a Multi-Level Feature Fusion Module (MFFM), the decoded network's multi-layer features are strengthened by a fusion mechanism, enhancing the interaction and complementarity between different levels of features, reducing the model's sensitivity to noise and non-critical information, and further enhancing the model's recognition accuracy and robustness.

Keywords

Weld Defect Detection, Dilated Hierarchical Attention Mechanism, Residual Fusion Module, Multi-Level Feature Fusion Module

Cite This Paper

Ruixiang Li, Shanwen Zhang, Lei Huang, Mingda Yang, Chengyu Hu. Enhanced Image Segmentation-Based Detection Technique for X-ray Film Images of Weld Seams. Academic Journal of Computing & Information Science (2024), Vol. 7, Issue 4: 118-128. https://doi.org/10.25236/AJCIS.2024.070416.

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