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一项基于分解合成方法的CT检测图像生成研究

Jintao Fu^1,2, Renjie Liu^1,2, Tianchen Zeng^1,2

¹Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China.

Journal of X-ray science and technology

|February 20, 2025

概括

一种新的分解合成方法 (DSM) 生成合成CT图像,以改善核反应堆组件的缺陷检测. 这种方法解决了小样本大小和类不平衡问题,提高了深度学习模型的准确性.

科学领域:

材料科学材料科学材料科学
非破坏性测试不破坏性测试
人工智能的人工智能

背景情况:

核石墨和碳组件对于高温气冷反应堆 (HTGR) 来说至关重要,确保结构完整性和安全运行.
螺旋式计算机断层扫描 (CT) 用于缺陷检测,但深度学习模型在有限和不平衡的缺陷数据集中扎.

研究的目的:

解决核反应堆组件缺陷检测模型培训中小样本大小和类不平衡的挑战.
通过生成大致的CT重建图像来增强缺陷检测培训数据集.

主要方法:

提出了分解合成方法 (DSM) 来生成合成CT图像.
DSM涉及:将CAD模型转换为voxel数据,重建CT图像,使用Contour-CycleGAN进行现实的图像生成,以及Copy-Adjust-Paste (CAP)用于缺陷合成.

主要成果:

使用DSM生成的数据集与实际CT图像的相似性更大.
与仅使用原始数据相比,使用DSM增强数据训练缺陷检测模型提高了检测准确性.

结论:

DSM有效地解决了缺陷检测中的小样本规模和类不平衡问题.
进一步优化生成算法和模型结构可以提高性能和准确性.

关键词:

CT检测图像生成图像的产生.轮-循环GANAN 的情况.复制 - 调整 - 粘贴 (CAP)分解合成合成方法 (DSM)

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