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28 March 2023 Weld defect recognition method based on improved DenseNet
Li Huadu, Luo Renze, Tang Xiang, Wu Yong, Li Yalong
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Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 1256629 (2023) https://doi.org/10.1117/12.2667731
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China
Abstract
There are many subjective influencing factors, poor recognition effect and low efficiency in manual evaluation of pipeline weld defects. An intelligent identification method of pipeline weld defects based on improved DenseNet network is proposed. This method firstly uses the form of multi-channel convolution of different scales to improve the DenseNet network, thereby improving the generalization ability of the network. Then, the feature extraction ability of the network is improved by stacking two convolutions of the same scale. Finally, an attention mechanism module is introduced into the dense connection block of the network to achieve the effect of improving beneficial features and suppressing useless features. The experimental results show that the method can achieve 92% accuracy in the identification of pipeline weld defects, which is about 13% higher than the original method, and has high efficiency, which can fully achieve the purpose of industrial application.
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Li Huadu, Luo Renze, Tang Xiang, Wu Yong, and Li Yalong "Weld defect recognition method based on improved DenseNet", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 1256629 (28 March 2023); https://doi.org/10.1117/12.2667731
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KEYWORDS
Convolution
Image segmentation
Data modeling
Education and training
Feature extraction
Performance modeling
Image processing
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