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A Deep Learning Model for Quick and Accurate Rock Recognition with Smartphones
Mobile Information Systems ( IF 1.863 ) Pub Date : 2020-05-19 , DOI: 10.1155/2020/7462524 Guangpeng Fan 1, 2 , Feixiang Chen 1, 2 , Danyu Chen 1, 2 , Yan Li 1, 2 , Yanqi Dong 1, 2
Mobile Information Systems ( IF 1.863 ) Pub Date : 2020-05-19 , DOI: 10.1155/2020/7462524 Guangpeng Fan 1, 2 , Feixiang Chen 1, 2 , Danyu Chen 1, 2 , Yan Li 1, 2 , Yanqi Dong 1, 2
Affiliation
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.
中文翻译:
使用智能手机快速,准确地识别岩石的深度学习模型
在地质调查中,岩石岩性的识别和分类是重要的内容。基于岩石薄片的识别方法识别周期长,识别成本高,不能保证识别的准确性。而且,上述方法在现场无法提供有效的解决方案。作为具有多个传感器的通信设备,智能手机由大多数地质调查人员携带。本文开发了一种基于卷积神经网络的智能手机应用程序。在此应用中,手机的相机可用于拍摄岩石的照片。而且,可以在很短的时间内快速,准确地识别出岩石的类型和岩性。提出了一种快速,准确地识别岩石岩性的方法。基于ShuffleNet,深度学习中使用的轻量级卷积神经网络,结合传递学习方法,建立了岩石图像的识别模型。然后将训练后的模型部署到智能手机。设计并开发了用于识别岩石岩性的智能手机应用程序,以验证其可用性和准确性。研究结果表明,在计算机验证数据集上,本文识别模型的准确性为97.65%。智能手机测试数据集的识别精度为95.30%,其中单张纸的平均识别时间为786毫秒,最大值为1,045毫秒,最小值为452毫秒。96%以上的单图像精度占测试数据集的95%。
更新日期:2020-05-19
中文翻译:
使用智能手机快速,准确地识别岩石的深度学习模型
在地质调查中,岩石岩性的识别和分类是重要的内容。基于岩石薄片的识别方法识别周期长,识别成本高,不能保证识别的准确性。而且,上述方法在现场无法提供有效的解决方案。作为具有多个传感器的通信设备,智能手机由大多数地质调查人员携带。本文开发了一种基于卷积神经网络的智能手机应用程序。在此应用中,手机的相机可用于拍摄岩石的照片。而且,可以在很短的时间内快速,准确地识别出岩石的类型和岩性。提出了一种快速,准确地识别岩石岩性的方法。基于ShuffleNet,深度学习中使用的轻量级卷积神经网络,结合传递学习方法,建立了岩石图像的识别模型。然后将训练后的模型部署到智能手机。设计并开发了用于识别岩石岩性的智能手机应用程序,以验证其可用性和准确性。研究结果表明,在计算机验证数据集上,本文识别模型的准确性为97.65%。智能手机测试数据集的识别精度为95.30%,其中单张纸的平均识别时间为786毫秒,最大值为1,045毫秒,最小值为452毫秒。96%以上的单图像精度占测试数据集的95%。