Abstract
Rock lithology recognition plays a fundamental role in geological survey research, mineral resource exploration, mining engineering, etc. However, the objectivity of researchers, rock variable natures, and tedious experimental processes make it difficult to ensure the accurate and effective identification of rock lithology. Additionally, multitype hybrid rock lithology identification is challenging, and few studies on this issue are available. In this paper, a novel multitype hybrid rock lithology detection method was proposed based on convolutional neural network (CNN), and neural network model compression technology was adopted to guarantee the model inference efficiency. Four fundamental single class rock datasets: sandstone, shale, monzogranite, and tuff were collected. At the same time, multitype hybrid rock lithologies datasets were obtained based on data augmentation method. The proposed model was then trained on multitype hybrid rock lithologies datasets. Besides, for comparison purposes, the other three algorithms, were trained and evaluated. Experimental results revealed that our method exhibited the best performance in terms of precision, recall, and efficiency compared with the other three algorithms. Furthermore, the inference time of the proposed model is twice as fast as the other three methods. It only needs 11 milliseconds for single image detection, making it possible to be applied to the industry by transforming the algorithm to an embedded hardware device or Android platform.
Highlights
Rock lithology classification has always been an indispensable part of engineering fields
Object detection algorithms based on convolutional neural network (CNN) possess stronger functions and broader applications
Given the computing resource cost, this paper developed a novel method for multitype hybrid rock lithology detection based on YOLO-V3
Summary
Rock lithology classification has always been an indispensable part of engineering fields. It is significant for engineers to understand in-situ rock lithology accurately and efficiently prior to engineering design and construction, excavation, and support schedules. In the past, it mainly depended on physical or chemical analysis methods. Technicians classified in-situ rock types by observing rock mineral composition and crystalline structure through magnifying glasses. They brought rock samples back to the laboratory, made thin sections, and analyzed the internal structure under the microscope to finish rock type classification. Convolutional neural networks (CNNs) have been widely used in many fields, including rock lithology classification. CNN is usually composed of three main modules: convolutional layers, activation layers, and pooling layers
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