当前位置: X-MOL 学术Scanning › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
A Hybrid Crack Detection Approach for Scanning Electron Microscope Image Using Deep Learning Method
Scanning ( IF 1.750 ) Pub Date : 2021-08-10 , DOI: 10.1155/2021/5558668
Lun Zhao 1, 2 , Yunlong Pan 3 , Sen Wang 3 , Liang Zhang 1 , Md Shafiqul Islam 4
Affiliation  

The scanning electron microscope (SEM) is widely used in the analysis and research of materials, including fracture analysis, microstructure morphology, and nanomaterial analysis. With the rapid development of materials science and computer vision technology, the level of detection technology is constantly improving. In this paper, the deep learning method is used to intelligently identify microcracks in the microscopic morphology of SEM image. A deep learning model based on image level is selected to reduce the interference of other complex microscopic topography, and a detection method with dense continuous bounding boxes suitable for SEM images is proposed. The dense and continuous bounding boxes were used to obtain the local features of the cracks and rotating the bounding boxes to reduce the feature differences between the bounding boxes. Finally, the bounding boxes with filled regression were used to highlight the microcrack detection effect. The results show that the detection accuracy of our approach reached 71.12%, and the highest mIOU reached 64.13%. Also, microcracks in different magnifications and in different backgrounds were detected successfully.

中文翻译:

一种使用深度学习方法扫描电子显微镜图像的混合裂纹检测方法

扫描电子显微镜(SEM)广泛应用于材料的分析和研究,包括断裂分析、微观结构形貌和纳米材料分析。随着材料科学和计算机视觉技术的飞速发展,检测技术水平不断提高。本文采用深度学习方法智能识别SEM图像微观形貌中的微裂纹。选择基于图像级别的深度学习模型来减少其他复杂微观地形的干扰,提出一种适用于SEM图像的密集连续边界框检测方法。使用密集连续的边界框获取裂缝的局部特征并旋转边界框以减少边界框之间的特征差异。最后,使用填充回归的边界框来突出微裂纹检测效果。结果表明,我们的方法检测准确率达到了71.12%,最高mIOU达到了64.13%。此外,成功检测到不同放大倍数和不同背景下的微裂纹。
更新日期:2021-08-10
down
wechat
bug