基于改进ConvNeXt网络的矿物图像智能识别  

Intelligent mineral image recognition based on improved ConvNeXt network

作　　者：李雷[1,2] 卢才武 江松[1,2] 景文刚 王洛锋[3] LI Lei;LU Caiwu;JIANG Song;JING Wengang;WANG Luofeng(College of Resource Engineering,Xi'an University of Architecture and Technology,Xi'an 710055,Shaanxi,China;Xi’an Key Laboratory for Intelligent Industrial Perception,Calculation and Decision,Xi'an University of Architecture and Technology,Xi’an 710055,Shaanxi,China;Luoyang Luanchuan Molybdenum Industry Group Co.,Ltd.,Luoyang 471500,Henan,China)

机构地区：[1]西安建筑科技大学资源工程学院,陕西西安710055 [2]西安市智慧工业感知、计算与决策重点实验室,陕西西安710055 [3]洛阳栾川钼业集团股份有限公司,河南洛阳471500

出　　处：《地质通报》2024年第7期1266-1275,共10页Geological Bulletin of China

基　　金：国家自然科学基金面上项目《地下金属矿山岩体破坏多源异质流数据智能融合与态势评估研究》(批准号:51974223);国家自然科学基金青年项目《基于数据-知识混合驱动的露天矿复杂边坡灾害识别与预警》(批准号:52104146)。

摘　　要：矿物识别是地质研究的重要工作,但是如何准确识别矿物仍然是一项重要的挑战。针对矿物形态特征,提出了一种利用迁移学习策略并引入通道注意力的改进ConvNeXt网络矿物图像智能识别模型。首先,利用ImageNet数据集上已预训练的ConvNeXt网络模型,运用迁移学习的方式,加载到矿物识别模型中;其次,在ConvNeXt网络的基础上,以ConvNeXt块之后与注意力机制相结合的方式,进一步提升其特征融合能力;最后,以26类矿物的矿石图像为研究对象,总计34576张图像,以6∶2∶2比例划分训练集、验证集与测试集,模型在实验训练过程中与VGG19、GoogLeNet、ResNet50、ResNeXt50和ConvNeXt网络相比,收敛速度明显加快。实验结果表明,矿物智能识别模型在准确率、精确率和召回率上分别达到98.58%、98.62%和98.73%,而消融实验证明本文提出的优化方法有助于提升模型性能,同时,通过对不同模型矿物图像特征图的可视化对比分析,验证了本文提出的矿物识别模型对于矿物特征的准确提取,进一步证明了模型的有效性,提高了矿物识别的准确率。Mineral identification is a critical task in geological research,yet accurately identifying minerals remains a significant challenge.This study proposes an intelligent mineral image recognition model based on an improved ConvNeXt network,which utilizes transfer learning strategies and incorporates channel attention mechanisms to address the morphological characteristics of minerals.Firstly,the ConvNeXt network model pre-trained on the ImageNet dataset is employed and integrated into the mineral recognition model through transfer learning.Secondly,based on the ConvNeXt network,the model enhances feature fusion capabilities by combining the ConvNeXt blocks with attention mechanisms.Finally,a dataset comprising 34576 ore images of 26 mineral categories is used,divided into training,validation,and test sets in a 6∶2∶2 ratio.During experimental training,the proposed model demonstrates a significantly faster convergence compared to VGG19,GoogLeNet,ResNet50,ResNeXt50,and the ConvNeXt networks.Experimental results indicate that the intelligent mineral recognition model achieves an accuracy,precision,and recall of 98.58%,98.62%,and 98.73%,respectively.Ablation experiments confirm that the optimization methods proposed in this study enhance model performance.Additionally,comparative visual analysis of feature maps from different models substantiates that the proposed mineral recognition model accurately extracts mineral features,further validating the model's effectiveness and improving mineral identification accuracy.

关 键 词：矿物图像 ConvNeXt 迁移学习 注意力机制 矿物识别 

分 类 号：P57[天文地球—矿物学] P628[天文地球—地质学]