Zno SEM Image Segmentation Based on Deep Learning. Issue 2 (March 2020)

• Record Type: Journal Article
• Authors: Li, Linmao
• Is Part Of: IOP conference series. Volume 782:Issue 2(2020)
• Publication Date: 2020-03
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/782/2/022035
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: The radius of nanomaterials, which will affect the specific surface area of the nanowires and other functions, is important for the optoelectronic application of nanomaterials. The Scanning Electron Microscopy (SEM) is an effective method to observe the spatial morphology of nanowires. However, the current measurement of topographical features mainly uses manual methods, which will bring about instability errors, especially when measuring a large number of them. Deep learning provides an efficient, fast way to identify and segment nanowire SEM images. Through deep learning methods, the spatial characteristics of nanomaterials can be measured quickly, which aim to explain the relationship between features and radio and television applications from a statistical point of view. In this paper, we design a deep learning image recognition system to measure the radius.