DSnet: x-ray weld defect segmentation method based on deep separable convolution structure

Meng Wang; Zhigang Lv; Peng Wang; Bei Ma; Liangliang Li; Yuntao Xu; Xiaoyan Li

doi:10.1117/12.3038587

23 August 2024 DSnet: x-ray weld defect segmentation method based on deep separable convolution structure

Meng Wang, Zhigang Lv, Peng Wang, Bei Ma, Liangliang Li, Yuntao Xu, Xiaoyan Li

Proceedings Volume 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024); 132501S (2024) https://doi.org/10.1117/12.3038587
Event: 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), 2024, Kuala Lumpur, Malaysia

Abstract

Due to the characteristics of multi-scale and complex edge shape of X-ray film weld defects, the segmentation accuracy of conventional segmentation methods is low. Aiming at the characteristics of these defects, this paper proposes an X-ray weld defect segmentation method based on deep separable convolution structure. Firstly, a multi-channel information fusion module is innovatively designed to replace the two consecutive 3×3 convolutions. Secondly, a spatial depth separation attention mechanism is added after the skip connection. Finally, a tail feature information cascade module is proposed to fuse different levels of feature information. This method can show high performance when segmenting X-ray film weld defects with multi-scale and complex edge shapes. The experimental results on the public dataset GDX-ray show that the proposed method is 6.1 % and 6.4 % higher than the classical Unet method in DSC value and precision P respectively, which effectively improves the segmentation accuracy of X-ray weld defects.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Meng Wang, Zhigang Lv, Peng Wang, Bei Ma, Liangliang Li, Yuntao Xu, and Xiaoyan Li "DSnet: x-ray weld defect segmentation method based on deep separable convolution structure", Proc. SPIE 13250, Fourth International Conference on Image Processing and Intelligent Control (IPIC 2024), 132501S (23 August 2024); https://doi.org/10.1117/12.3038587

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KEYWORDS

Convolution

X-rays

Image segmentation

Information fusion

Feature extraction

Defect detection

Network architectures

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