A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones
Joint Authors
Fan, Guangpeng
Dong, Yanqi
Chen, Danyu
Chen, Feixiang
Li, Yan
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-19
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Telecommunications Engineering
Abstract EN
In the geological survey, the recognition and classification of rock lithology are an important content.
The recognition method based on rock thin section leads to long recognition period and high recognition cost, and the recognition accuracy cannot be guaranteed.
Moreover, the above method cannot provide an effective solution in the field.
As a communication device with multiple sensors, smartphones are carried by most geological survey workers.
In this paper, a smartphone application based on the convolutional neural network is developed.
In this application, the phone’s camera can be used to take photos of rocks.
And the types and lithology of rocks can be quickly and accurately identified in a very short time.
This paper proposed a method for quickly and accurately recognizing rock lithology in the field.
Based on ShuffleNet, a lightweight convolutional neural network used in deep learning, combined with the transfer learning method, the recognition model of the rock image was established.
The trained model was then deployed to the smartphone.
A smartphone application for identifying rock lithology was designed and developed to verify its usability and accuracy.
The research results showed that the accuracy of the recognition model in this paper was 97.65% on the verification data set of the PC.
The accuracy of recognition on the test data set of the smartphone was 95.30%, among which the average recognition time of the single sheet was 786 milliseconds, the maximum value was 1,045 milliseconds, and the minimum value was 452 milliseconds.
And the single-image accuracy above 96% accounted for 95% of the test data set.
This paper presented a new solution for the rapid and accurate recognition of rock lithology in field geological surveys, which met the needs of geological survey personnel to quickly and accurately identify rock lithology in field operations.
American Psychological Association (APA)
Fan, Guangpeng& Chen, Feixiang& Chen, Danyu& Li, Yan& Dong, Yanqi. 2020. A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1192459
Modern Language Association (MLA)
Fan, Guangpeng…[et al.]. A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones. Mobile Information Systems No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1192459
American Medical Association (AMA)
Fan, Guangpeng& Chen, Feixiang& Chen, Danyu& Li, Yan& Dong, Yanqi. A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1192459
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1192459