| 钟翼龙,张晓凤,刘祎斌,王小荣.填装材料CT图像中脱粘缺陷识别与深度测量[J].电子测量与仪器学报,2024,38(1):178-186 |
| 填装材料CT图像中脱粘缺陷识别与深度测量 |
| Identification and depth measurement of debonding defectsin CT images of packing materials |
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| DOI： |
| 中文关键词: SoftMax 活动轮廓模型 脱粘缺陷识别与分割 脱粘深度测量 填装材料 |
| 英文关键词:SoftMax active contour model debonding identification and segmentation debonding depth measurement packing materials |
| 基金项目:重庆市自然科学基金面上项目（CSTB2022NSCQ-MSX1209）、重庆科技学院人才引进科研启动项目（ckrc2021045）、重庆科技学院科技创新项目（YKJCX2120423）资助 |
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| Author | Institution |
| Zhong Yilong | School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China |
| Zhang Xiaofeng | School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China |
| Liu Yibin | School of Electrical Engineering, Chongqing University of Science and Technology, Chongqing 401331, China |
| Wang Xiaorong | School of Civil Engineering and Architecture, Chongqing University of Science and Technology, Chongqing 401331, China |
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| 中文摘要: |
| 脱粘缺陷是影响填装材料物理安全性能的重要指标,计算机层析成像(CT)是进行脱粘缺陷检测的有效方法。但填装材料脱粘缺陷紧贴外轮廓、面积小,对比度低,分割时易受其他信息干扰,传统活动轮廓模型难以适用。本研究提出了一种基于SoftMax和正则项的Chan-Vese (SRCV)模型用于填装材料脱粘缺陷分割,系统比较了SRCV模型与多种活动轮廓模型对脱粘缺陷分割效果的差异,利用不同图片分割效果揭示了SRCV模型的抗干扰能力和兼顾全局与局部信息的能力,利用分割曲线的欧氏距离实现脱粘深度测量。研究结果表明，SRCV模型对填装材料脱粘缺陷分割更贴近脱粘边缘,分割曲线更光滑,准确度达99.56%,Dice系数为99.82%,脱粘深度误差不超过6%,特别适用于有大量干扰信息的微小脱粘缺陷的分割,较其他活动轮廓模型具有明显的优势。 |
| 英文摘要: |
| The debonding defect is a crucial indicator affecting the physical safety performance of packing materials, and computed tomography (CT) stands as an effective method for its detection. However, due to the debonding defects’ close to the outer contour and small area, low contrast, they are susceptible to interfere from other information during segmentation, making traditional active contour models less suitable. In order to segment the packing materials debonding defects, SoftMax and regular term Chan Vese (SPCV) model based on Chan-Vese is proposed by introducing SoftMax and regular term in this paper. Differences in segmentation effects between the SRCV model and various active contour models were systematically compared. Segmentation results on different images are utilized to demonstrate anti-interference capabilities and balance global and local information capabilities of SRCV model. The depth of debonding was measured using Euclidean distance. When the SRCV model is used to segment the debonding defects of packing materials, the segmentation curves are closer to the debonding edges and smoother. The accuracy and the Dice coefficient were 99.56% and 99.82%, respectively. And the error of the debonding depth was not more than 6%. The results show that the SRCV model is particularly suitable for the segmentation of tiny debonding defects with a large amount of interfering information, and has obvious advantages over other active contour models. |
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