A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones

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.

[1]  Claudio A. Perez,et al.  Classification of rock lithology by laser range 3D and color images , 2017 .

[2]  Claudio A. Perez,et al.  Rock lithological classification using multi-scale Gabor features from sub-images, and voting with rock contour information , 2015 .

[3]  Hossein Izadi,et al.  An intelligent system for mineral identification in thin sections based on a cascade approach , 2017, Comput. Geosci..

[4]  Chuang Liu,et al.  A Plant Leaf Geometric Parameter Measurement System Based on the Android Platform , 2019, Sensors.

[5]  Guojian Cheng,et al.  Rock images classification by using deep convolution neural network , 2017 .

[6]  H. Massonne,et al.  SIMPLE IDENTIFICATION AND QUANTIFICATION OF MICRODIAMONDS IN ROCK THIN-SECTIONS , 1998 .

[7]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[8]  Le Yu,et al.  Towards automatic lithological classification from remote sensing data using support vector machines , 2010, Comput. Geosci..

[9]  François Laviolette,et al.  Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..

[10]  Ari Visa,et al.  Rock image classification using color features in Gabor space , 2005, J. Electronic Imaging.

[11]  Snehamoy Chatterjee,et al.  Computer vision-based limestone rock-type classification using probabilistic neural network , 2016 .

[12]  Heinrich F. Arlinghaus,et al.  Characterization and identification of minerals in rocks by ToF-SIMS and principal component analysis , 2015 .

[13]  Naresh Singh,et al.  Textural identification of basaltic rock mass using image processing and neural network , 2010 .

[14]  Hasan Badem,et al.  Performance improvement of deep neural network classifiers by a simple training strategy , 2018, Eng. Appl. Artif. Intell..

[15]  Krzysztof Wolk,et al.  Deep Learning in State-of-the-Art Image Classification Exceeding 99% Accuracy , 2019, WorldCIST.

[16]  Dong Wang,et al.  An automatic methodology for analyzing sorting level of rock particles , 2018, Computational Geosciences.

[17]  Pejman Tahmasebi,et al.  Segmentation of digital rock images using deep convolutional autoencoder networks , 2019, Comput. Geosci..

[18]  Frank S. Reed,et al.  Preparation of rock thin sections , 1953 .

[19]  Doo Seop Eom,et al.  Smartphone-Based Traveled Distance Estimation Using Individual Walking Patterns for Indoor Localization , 2018, Sensors.

[20]  Na Li,et al.  A transfer learning method for automatic identification of sandstone microscopic images , 2017, Comput. Geosci..

[21]  Roberto Tagliaferri,et al.  Textural identification of carbonate rocks by image processing and neural network: Methodology proposal and examples , 2005, Comput. Geosci..