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A Hybrid Crack Detection Approach for Scanning Electron Microscope Image Using Deep Learning Method
Shenzhen Polytech, CHN.
Kunming Univ, CHN.
Kunming Univ, CHN.
Shenzhen Polytech, CHN.
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2021 (English)In: Scanning, ISSN 0161-0457, E-ISSN 1932-8745, article id 5558668Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Wiley-Hindawi , 2021. article id 5558668
Keywords [en]
HIGH-RESOLUTION, SEGMENTATION, SEM, ALLOY
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:bth-22103DOI: 10.1155/2021/5558668ISI: 000687437800001Scopus ID: s2.0-85113815525OAI: oai:DiVA.org:bth-22103DiVA, id: diva2:1590736
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open access

Available from: 2021-09-03 Created: 2021-09-03 Last updated: 2021-09-16Bibliographically approved

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A Hybrid Crack Detection Approach for Scanning Electron Microscope Image Using Deep Learning Method(3749 kB)110 downloads
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Islam, Md. Shafiqul

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