A rock classification system based on embedded platform Online publication date: Wed, 11-Oct-2023
by Xiangyuan Zhu; Jie Yang; Weiyang Zhi; Haifeng Lu
International Journal of Embedded Systems (IJES), Vol. 16, No. 1, 2023
Abstract: Rock is a major material for the crust and upper mantle formation of the Earth. In Earth Sciences, rock image classification is an essential and critical task in the geological survey. Due to the scarcity of samples and unaffordability of rock classification systems, an embedded system was built to collect and identify rock images. The Raspberry Pi3B+ was applied as the micro controller unit and the Sony IMX219 image sensor was selected to shoot rock images. The new well-annotated dataset contains seven types of fresh rocks with 7,976 images. Based on the new dataset, a new rock classification model based on the ConvNeXt algorithm was proposed. To ensemble the local and global features of the rock images, a feature fusion strategy named super-image was designed. Compared with the prevalent models including VGG16, ResNet50, MobileNet V3, GoogleNet, and DenseNet121, our enhanced ConvNeXt method achieved the macro-average F1 and accuracy of 99.61% and 99.63%, respectively.
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