Author:
LYU 吕 Jianhua 建骅,NIU 牛 Chunjie 春杰,CUI 崔 Yunqiu 运秋,CHEN 陈 Chao 超,NI 倪 Weiyuan 维元,FAN 范 Hongyu 红玉
Abstract
Abstract
Observing and analyzing surface images is critical for studying the interaction between plasma and irradiated plasma-facing materials. This paper presents a method for the automatic recognition of bubbles in transmission electron microscope (TEM) images of W nanofibers using image processing techniques and convolutional neural network (CNN). We employ a three-stage approach consisting of Otsu, local-threshold, and watershed segmentation to extract bubbles from noisy images. To address over-segmentation, we propose a combination of area factor and radial pixel intensity scanning. A CNN is used to recognize bubbles, outperforming traditional neural network models such as AlexNet and GoogleNet with an accuracy of 97.1% and recall of 98.6%. Our method is tested on both clear and blurred TEM images, and demonstrates human-like performance in recognizing bubbles. This work contributes to the development of quantitative image analysis in the field of plasma-material interactions, offering a scalable solution for analyzing material defects. Overall, this study’s findings establish the potential for automatic defect recognition and its applications in the assessment of plasma-material interactions. This method can be employed in a variety of specialties, including plasma physics and materials science.
Funder
Dalian Science and Technology Star Project
Fundamental Research Funds for the Central Universities
Central Guidance on Local Science and Technology Development Fund of Liaoning Province
National Key Research and Development Program of China