Classification of Petrographic Thin Section Images With Depthwise Separable Convolution and Dilated Convolution

To enhance the precision and efficiency of petrographic thin section image classification and reduce the subjectivity resulting from manual classification methods, a new classification model (DC-PC-Dilated-IR-V2) in term of the deep convolutional network is constructed in this study. In the DC-PC-Di...

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Bibliografische Detailangaben

Hauptverfasser:	Shaowei Pan, Xingxing Cheng, Wenjing Fan
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	IEEE 2025-01-01
Schriftenreihe:	IEEE Access
Schlagworte:	Petrographic thin section image classification depthwise separable convolution attention module dilated convolution
Online-Zugang:	https://ieeexplore.ieee.org/document/10879401/
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