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A Defect Recognition Method for Low-Quality Weld Image Based on Consistent Multiscale Feature Mapping
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AI
DOI：10.1109/TIM.2022.3171609.png)
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Abstract
En 中文
Defect recognition is an essential technology for the safety of pipelines. Recently, deep-learning (DL)-based methods have shown great progress in recognizing pipeline weld defects. However, when it comes to low-quality images in some complicated applications, the DL-based methods can hardly achieve satisfactory results. To solve this problem, this article proposes a consistent multiscale feature mapping method for defect recognition of low-quality weld images. First, a multiscale feature mapping method is proposed, so that different types of defect images can be mapped into distinctive features in both local and global embedding spaces. Second, a novel consistency strategy is proposed to maintain the consistency of the local and global embedding spaces, so that similar defects can be easily distinguished. Third, a special feature fusion model is proposed to fuse the mapped local and global features, so that the performance of defect recognition can be improved. Finally, five groups of experiments are conducted using pipeline weld X-ray images and metal weld X-ray images. The experiment results show that the proposed method is effective in recognizing the weld defects of low-quality X-ray images.
Keywords:
Feature extraction
Image recognition
Welding
X-ray imaging
Pipelines
Measurement
Deep learning
Consistency strategy
feature fusion
low-quality X-ray image
multiscale feature mapping
weld defect recognition
Journal
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5.9
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2.0W
Citations:
5.8W
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