岩石薄片的岩性识别是地质分析中不可或缺的一环,其精准度直接影响后续地层岩石种类、性质和矿物成分等信息的确定,对于地质勘探和矿产开采具有重要意义.为了快速准确地识别岩性,本文提出了一种改进的MobileNetV2轻量化模型,通过选取5种岩石类型共3 700张岩石薄片图像进行岩性识别.在MobileNetV2的倒残差结构中嵌入坐标注意力机制,融合图像中多种矿物的全局特征信息.此外,改进MobileNetV2中的分类器,降低模型的参数量和计算复杂度,从而提高模型的运算速度和效率,并采用带泄露线性整流函数(leaky rectified linear unit,Leaky ReLU)作为激活函数,避免网络训练中的梯度消失问题.实验结果表明,本文提出的改进后的MobileNetV2模型大小仅为2.30 MB,在测试集上的精确率、召回率、F1值分别为91.24％、90.18％、90.70％,具有较高的准确性,相比于SqueezeNet、ShuffleNetV2等同类型的轻量化网络,分类效果最好.
Abstract
The lithology identification of rock thin sections is an indispensable part of geological analysis,and its precision directly affects the determination of the types,properties,mineral composition,and other microscopic information of subsequent stratigraphic rock,which is of great significance for geological exploration and mineral mining.In order to identify lithology quickly and accurately,an improved MobileNetV2 lightweight model is proposed to address the complex and diverse mineral composition in rock slices,which identifies lithology from a total of 3 700 rock slice images of five types of rocks.The coordinate attention mechanism is embedded in the inverse residual structure of MobileNetV2 to fuse global feature information of multiple minerals in the image.In addition,the classifier in MobileNetV2 is improved to reduce the number of parameters and computational complexity of the model,so as to improve the computing speed and efficiency of the model,and the leaky rectified linear unit(Leaky ReLU)is used as the activation function to avoid the problem of gradient vanishing in network training.Experimental results show that the improved MobileNetV2 model proposed in this paper has a size of only 2.30 MB,and the precision,recall rate,and F1 value on the test set are 91.24％,90.18％,and 90.70％,respectively,which has high accuracy,and has the best classification effect compared with similar lightweight networks such as SqueezeNet and ShuffleNetV2.
关键词
岩石薄片图像/轻量化神经网络/MobileNetV2/坐标注意力机制/岩性识别
Key words
rock thin section image/lightweight neural network/MobileNetV2/coordinate attention mechanism/lithology identification
维普
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