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Article(id=1241064280829776335, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241064275599479114, articleNumber=null, orderNo=null, doi=10.3969/j.issn.0253-6099.2025.05.007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1745078400000, receivedDateStr=2025-04-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773822382409, onlineDateStr=2026-03-18, pubDate=1759248000000, pubDateStr=2025-10-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773822382409, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773822382409, creator=13701087609, updateTime=1773822382409, updator=13701087609, issue=Issue{id=1241064275599479114, tenantId=1146029695717560320, journalId=1235980550691926019, year='2025', volume='45', issue='5', pageStart='1', pageEnd='201', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773822381162, creator=13701087609, updateTime=1773822785847, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241065973038501946, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241064275599479114, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241065973038501947, tenantId=1146029695717560320, journalId=1235980550691926019, issueId=1241064275599479114, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=41, endPage=48, ext={EN=ArticleExt(id=1241064281190486506, articleId=1241064280829776335, tenantId=1146029695717560320, journalId=1235980550691926019, language=EN, title=Homogenization Evaluation for Backfill Slurry Based on Improved Yolo-v7 Algorithm, columnId=1236276106018484431, journalTitle=Mining and Metallurgical Engineering, columnName=MINING, runingTitle=null, highlight=null, articleAbstract=

Homogenization of backfill slurry cannot be automatically evaluated during stirring process. To solve this problem, the Yolo-v7 algorithm was improved by using the CBAM and the SPD-Conv, and the improved Yolo-v7 algorithm was then employed to automatically evaluate the homogenization of backfill slurry. The research results show that the improved Yolo-v7 algorithm demonstrates a significantly improved performance, with the accuracy, recall rate and mean average precision (mAP) all increased by 17.5, 28.8, and 32.4 percentage points respectively. Analysis of slurry parameter sensitivity indicates that slurry concentration is the principal factor affecting evaluation of slurry inhomogeneity, followed by the cement-tailings ratio. Moreover, the homogeneity of backfill slurry with high concentration can be significantly improved by prolonging stirring time.

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为解决充填料浆搅拌过程中均质化程度无法自动判断的问题，采用CBAM注意力模块和SPD-Conv小目标检测模块对Yolo-v7算法进行改进，并基于改进后的Yolo-v7算法对充填料浆均质化进行自动判断。研究结果表明，改进后的Yolo-v7算法在性能上较原始模型有显著提升，其准确率、召回率和平均精度均值分别提高了17.5、28.8和32.4百分点。料浆参数敏感性分析结果表明，料浆浓度是影响料浆非均质化识别的主要因素，其次是灰砂比，而延长搅拌时间在高料浆浓度条件下可显著提升充填料浆的均质化水平。

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郭进平（1970—），男，湖北当阳人，副教授，硕士研究生导师，主要从事安全管理决策及矿山安全的科研与教学工作。E-mail：

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郭进平（1970—），男，湖北当阳人，副教授，硕士研究生导师，主要从事安全管理决策及矿山安全的科研与教学工作。E-mail：

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郭进平（1970—），男，湖北当阳人，副教授，硕士研究生导师，主要从事安全管理决策及矿山安全的科研与教学工作。E-mail：

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Results of different attention mechanisms

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注意力机制准确率/%召回率/%平均精度均值/%
Yolo-v7-CBAM69.270.667.8
Yolo-v7-CA65.559.258.5
Yolo-v7-ECA61.267.461.5
Yolo-v7-SimAM70.755.958.3
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注意力机制运行结果

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注意力机制准确率/%召回率/%平均精度均值/%
Yolo-v7-CBAM69.270.667.8
Yolo-v7-CA65.559.258.5
Yolo-v7-ECA61.267.461.5
Yolo-v7-SimAM70.755.958.3
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Comparison of ablation experiments

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算法名称CBAMSPD-Conv准确率/%召回率/%平均精度均值/%
Yolo-v7无无57.838.333.9
有无69.270.667.8
无有62.041.844.8
有有75.367.166.3
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消融实验情况对比

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算法名称CBAMSPD-Conv准确率/%召回率/%平均精度均值/%
Yolo-v7无无57.838.333.9
有无69.270.667.8
无有62.041.844.8
有有75.367.166.3
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基于改进Yolo-v7算法的充填料浆均质化检测方法
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郭进平 , 孙源泽 , 张超 , 王小林 , 李想 , 孔德浩
矿冶工程杂志 | 采矿 2025,45(5): 41-48
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矿冶工程杂志 | 采矿 2025, 45(5): 41-48
基于改进Yolo-v7算法的充填料浆均质化检测方法
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郭进平 , 孙源泽, 张超, 王小林, 李想, 孔德浩
作者信息
  • 西安建筑科技大学　资源工程学院，陕西　西安　710055

    • 郭进平（1970—），男，湖北当阳人，副教授，硕士研究生导师，主要从事安全管理决策及矿山安全的科研与教学工作。E-mail：

Homogenization Evaluation for Backfill Slurry Based on Improved Yolo-v7 Algorithm
Jinping GUO , Yuanze SUN, Chao ZHANG, Xiaolin WANG, Xiang LI, Dehao KONG
Affiliations
  • School of Resource Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China
出版时间: 2025-10-01 doi: 10.3969/j.issn.0253-6099.2025.05.007
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摘要
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为解决充填料浆搅拌过程中均质化程度无法自动判断的问题，采用CBAM注意力模块和SPD-Conv小目标检测模块对Yolo-v7算法进行改进，并基于改进后的Yolo-v7算法对充填料浆均质化进行自动判断。研究结果表明，改进后的Yolo-v7算法在性能上较原始模型有显著提升，其准确率、召回率和平均精度均值分别提高了17.5、28.8和32.4百分点。料浆参数敏感性分析结果表明，料浆浓度是影响料浆非均质化识别的主要因素，其次是灰砂比，而延长搅拌时间在高料浆浓度条件下可显著提升充填料浆的均质化水平。

充填料浆  /  Yolo-v7算法  /  非均质特征  /  均质化  /  智能检测  /  机器学习  /  机器视觉

Homogenization of backfill slurry cannot be automatically evaluated during stirring process. To solve this problem, the Yolo-v7 algorithm was improved by using the CBAM and the SPD-Conv, and the improved Yolo-v7 algorithm was then employed to automatically evaluate the homogenization of backfill slurry. The research results show that the improved Yolo-v7 algorithm demonstrates a significantly improved performance, with the accuracy, recall rate and mean average precision (mAP) all increased by 17.5, 28.8, and 32.4 percentage points respectively. Analysis of slurry parameter sensitivity indicates that slurry concentration is the principal factor affecting evaluation of slurry inhomogeneity, followed by the cement-tailings ratio. Moreover, the homogeneity of backfill slurry with high concentration can be significantly improved by prolonging stirring time.

backfill slurry  /  Yolo-v7 algorithm  /  inhomogeneous property  /  homogenization  /  intelligent evaluation  /  machine learning  /  machine vision
郭进平, 孙源泽, 张超, 王小林, 李想, 孔德浩. 基于改进Yolo-v7算法的充填料浆均质化检测方法. 矿冶工程杂志, 2025 , 45 (5) : 41 -48 . DOI: 10.3969/j.issn.0253-6099.2025.05.007
Jinping GUO, Yuanze SUN, Chao ZHANG, Xiaolin WANG, Xiang LI, Dehao KONG. Homogenization Evaluation for Backfill Slurry Based on Improved Yolo-v7 Algorithm[J]. Mining and Metallurgical Engineering, 2025 , 45 (5) : 41 -48 . DOI: 10.3969/j.issn.0253-6099.2025.05.007
正文
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金属矿全尾砂料浆充填具有安全、环保、经济和高效等优点[1]，在大量消耗大宗固废、减少环境污染的同时[2-3]，可有效消除采空区和尾矿库安全隐患[4]。充填料浆搅拌是充填的第二大关键工艺环节，料浆均质化程度直接影响料浆的管道输送性能和硬化后的力学性能[5-6]。充填料浆搅拌不充分，会导致充填料浆内部的固体颗粒分布不均匀，充填料浆在输送和充填过程中发生分层、离析和泌水等现象，出现堵管、爆管等恶劣情况，并会造成充填体强度不均等问题，影响充填效果[7-8]。
目前，充填料浆均质化程度以及是否达到均质状态仍依赖人工进行判断，人力消耗大，数据反馈处理时间长。随着计算机视觉技术的不断发展[9]，已有学者对相关领域进行研究，文献[10]通过激光颗粒显微成像技术（PVM）开展絮凝沉降过程絮团结构演变规律的研究，提出用等效直径来定量化表征细观结构特征，反映充填料浆结构演化规律；文献[11]通过深度学习算法，采用Gamma矫正策略对充填料浆图像数据集进行学习，实现了非接触式膏体浓度识别方法；文献[12]采用基于Radam算法优化ResNet50模型充填料浆图像识别方法，实现对充填料浆在浆体、膏体、滤饼等3种不同状态下的识别；文献[13]使用电阻层析成像技术（ERT），实时获得流场的颗粒分布，分析了剪切作用下充填料浆的流动状态；此外，文献[14-15]也进行了相关研究。
由以上研究可知，现有的计算机视觉算法应用于充填料浆状态自动化检测领域，大多集中在充填料浆状态及浓度方面，针对料浆的均质化搅拌检测的研究相对较少。因此，为了进一步提高对料浆均质化检测的判断精度，本文基于文献[16-18]的研究方法，提出了一种基于Yolo-v7的改进算法，用于自动化检测充填料浆均质化程度。
1　实验流程及数据集处理
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1.1　实验材料与流程
使用P.O 42.5硅酸盐水泥和某铅锌矿超细全尾砂制备充填料浆。尾砂粒径分布如图1所示，-20 μm粒级占比55.4%，-74 μm粒级占比92.8%，加权平均粒径为25.8 μm。尾砂不均匀系数为7.35，曲率系数为0.86，级配不良。
实验制备料浆灰砂比（水泥与尾砂质量比）分别为1∶4、1∶6、1∶8，料浆浓度（质量分数，下同）分别选取40%、60%、80%，采用“先干拌后湿拌”的方式进行充填料浆制备。从尾砂水泥混合材料中倒入自来水开始进行料浆搅拌过程均匀状态录制，从未搅拌状态录制至完全搅拌均匀状态得到所需数据集视频，然后通过数据集处理，得到所需料浆搅拌数据集进行机器视觉深度学习。
1.2　数据集获取与处理
将浓度高于80%的料浆称为固体料浆，浓度60%～80%的料浆称为膏体料浆，浓度低于60%的料浆称为液态料浆。使用录像设备拍摄4K分辨率60帧的充填料浆搅拌视频图像，获取视频图像后使用PotPlayer软件将视频进行每秒一帧的分帧处理[19]。
本实验共拍摄不同浓度的尾砂料浆搅拌视频208 min，获取数据集3 283张。按照7∶2∶1的比例将数据集分为学习集、测试集和验证集，得到学习集2 298张、测试集657张、验证集328张。
使用Labelimg开源特征标注工具对学习集进行人工特征标注，制定三类非均质特征标签分别为Ripple（液体状）、Caking（结块状）、Arid（固态状），料浆搅拌至均质时，水泥尾砂均匀分布，此时无法检测出非均质特征，当不存在三类标签其中的任意一种时，料浆即处于均质状态。三类标签示例如图2所示。
在Python环境内下载LabelImg特征标注软件，首先通过“Open Dir”按钮选择包含待标注图像的目录，并设置标注文件的保存路径。使用鼠标拖动创建边界框，保证特征框紧贴特征，以保证特征识别的准确率。然后输入相应的特征标签名称。标注格式为xml格式，用于机器学习；对测试集进行人工标注，用于优化参数；验证集不进行人工标注，用于检验网络学习结果。人工标注示例如图3所示。
1.3　实验环境与评价标准
采用Windows11操作系统，应用PyTorch深度学习框架，中央处理器为12th Gen Intel（R）Core（TM）i5-12490F，图像处理器为NVIDA GeForce RTX 4060（8.00 GB），编程语言为Python，在PyCharm环境下运行[20]。
本实验均质化标准采取非均质化判断方法，识别充填料浆在不同状态下存在液体、结块、固态三种非均质化状态，当识别中出现非均质检测框，且检测框随着搅拌状态的变化消失时，认为充填料浆已搅拌至均质状态。
本实验选取检测平均精度（average precision，AP）、均值平均精度（mean average precision，mAP）、每秒检测图像帧数（frame per second，FPS）、模型大小、Giga浮点运算每秒作为评价指标。计算公式如下：
式中：P表示精度，即模型检测出的正类占总体的百分比，用于体现模型预测的准确度；R表示召回率，即实际正类中有多少被成功检测到，用于体现模型检测的全面性；TP表示正类预测；FP表示负类预测；FN表示错误预测为负类的正类样本；P（R）表示在召回率R下对应的精确率P；ZAP表示平均精度值；ZAPi表示第i个类别的ZAP值；ZmAP表示均值平均精度值，可以衡量模型的检测性能，用于综合评估多类别检测性能。
2　Yolo-v7模型
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2.1　Yolo-v7模型介绍
Yolo-v7模型是基于Yolo-v5[21]提出的新一代模型，主要由Backbone（骨干网络）、Neck（颈部）和Head（头部）三层结构构成。相较于Yolo-v5模型，Yolo-v7模型在骨干网络中使用ELAN模块，扩展具有高效率和程增强特点的专注力互联网模块。在颈部结构中采用MPA（multi-path aggregation）模块，通过多条路径的特征聚合，提高特征的丰富性。在头部结构中使用改进的锚点框设计，以更好地适应不同尺度和形状的目标。Yolo-v7模型相较前几代模型，具有更高的精密度和更快的识别速率，并且与移动端的图像处理器设备有更强的适配性，且具有在复杂背景下的鲁棒性、多尺度检测能力和轻量化部署等特点，在井下膏体搅拌自动检测中更具有优势[22]。
Yolo-v7模型主要由CBS、ELAN、MP、UPSample等模块构成。其中，CBS是由卷积层（Conv）+批量归一化层（BN）+激活函数层（SiLU）构成的卷积模块，Conv的作用是从输入的图像中提取有价值的特征信息，BN的作用是加速模型收敛速度，增强模型稳定性，SiLU的作用是提升模型的表达能力和泛化能力；ELAN模块是一个高效的网络结构，它通过控制梯度路径的长短，使网络能够更加精准地学习到更多的特征，使整体模型具有更强的鲁棒性；MP模块是使用2个下分支进行超级下采样的模块；UPSample模块是一个使用最近邻插值的上采样模块，其中各模块备注的3个数字分别代表了该模块输入特征通道数、输出特征通道数以及卷积核的大小。Yolo-v7模型的整体架构如图4所示。
2.2　Yolo-v7模型改进
为了提升Yolo-v7模型对膏体均质化特征的识别准确率，同时减轻算法复杂度，以获得更快的识别效率，本文在Yolo-v7模型中嵌入了注意力机制模块和小目标检测模块两种优化模型。
2.2.1　注意力机制模块
卷积块注意力机制（convolutional block attention module，CBAM）可以有效帮助深度神经网络抑制不显著的特征通道和特征空间，并利用不同维度特征的信息交互，以获取显著的特征。但是在料浆搅拌的过程中，各个特征表现相对不突出，所以使用注意力机制进行优化。
CBAM相比于其他注意力机制，如CA（coordinate attention）、SimAM（a simple，parameter-free attention module）、ECA（efficient channel attention），主要使用了通道注意力和空间注意力，获得不同的通道特征表示和不同空间中的特征信息，以便在面对同一特征的不同尺度、形状、方向信息时获得更高的精度。
CBAM中通道注意力使用全局最大池化（MaxPool）和全局平均池化（AvgPool）来聚合特征图的空间信息，然后通过全连接层（Shared MLP）进行特征提取和特征转换，生成相应的特征权重，这些权重通过Sigmoid激活函数进行归一化，并将得到的注意力权重与原始的注意力权重进行向量相交，得到最终的通道注意力。通道注意力计算过程为：
式中：F为输入特征图；σ为Sigmoid激活函数；AvgPool为全局平均池化操作；MaxPool为最大池化操作；MLP为全连接层。
空间注意力是使用全局最大池化和全局平均池化分别获取不同特征图的最大值和平均值，然后将所获得的特征图进行拼接，经过卷积层处理后获得空间注意力权重，这些权重同样通过Sigmoid激活函数进行归一化，并应用于最初的特征图，以调整不同空间位置的权重。空间注意力计算过程为：
式中：MLP_S为另外一个全连接网络；⊗为逐元素乘法。
通道注意力及空间注意力模块的实现过程如图5～6所示。
最后将通道注意力模块和空间注意力模块输出的不同权重进行逐元素相乘，得到最终的注意力增强特征：
式中：MC为通道注意力模块；MC（N）为通道注意力权重图；MS为空间注意力模块；MS（M）为空间注意力权重图；M为首次相乘操作权重图；Q为最终权重图[23-24]。混合模块如图7所示。
将CBAM、CA、SimAM、ECA四种注意力机制进行横向对比实验。实际生产过程中，需要高置信决策度，确保更精准识别膏体搅拌状态，使用准确率为主要参考指标、召回率和平均精度均值为辅助参考指标，选择适合膏体充填检测的注意力模块。注意力机制运行结果如表1所示。
通过表1可以看出，SimAM准确率最高，CBAM准确率低于SimAM准确率，但相差较小，相较于CA和ECA有明显优势。而CBAM的召回率和平均精度均值较其他3个注意力机制均有明显优势。表明CBAM注意力机制在识别膏体搅拌均质化方面更加合适。
2.2.2　SPD-Conv模块
充填料浆搅拌过程中，料浆的均质化状态、形态及表面纹理等物理特性会随着搅拌过程不断变化，导致特征识别难度提高。为了最大程度提高识别的准确性，本实验选择在算法中添加SPD-Conv模块。与传统CNN模块相比，SPD-Conv模块使用SPD层和非步长卷积层，替换CNN模块中的池化层和步长卷积层，在低分辨率下对图像和小目标的特征信息识别检测具有较大的优势。
SPD层是指空间到深度转换层，将输入图像的空间维度降低，转换成通道维度，保证输入图像信息不丢失的情况下能获得更多特征。Conv层是指非步长卷积层，输入图像信息经过SPD转换后，在不降低特征图尺寸的同时进行特征收集，进一步保持图像的细粒度信息。结合SPD层和Conv层可提高对低分辨率和小目标图像的识别精度[25]。
2.2.3　消融实验
为验证优化后算法的可靠性，本文设计了消融实验。消融实验通过系统地消融模型中的某些部分，得到不同组合对模型性能的影响。将不同的优化模块单独或组合加入原始算法中，用同一数据集分别进行实验，得到的数据结果如表2所示。
通过表2可以看出，添加CBAM模块和SPD-Conv模块的Yolo-v7算法，与基础Yolo-v7算法和只添加SPD-Conv模块的Yolo-v7算法相比，准确率、召回率、平均精度均值均有所提升；与只添加CBAM模块的Yolo-v7算法相比，虽然召回率和平均精度均值略有降低，但是准确率增幅更大。添加CBAM和SPD-Conv模块的组合，仍然获得了较好的检测效果。
2.3　实验结果检测
使用原始模型和添加CBAM、SPD-Conv模块的Yolo-v7模型对同一验证集进行检测，对固态、液态两种状态下的充填料浆进行图片识别，识别效果如图8～9所示。
通过图8～9可以看出，优化后的Yolo-v7模型具有更多的识别方框，说明优化后的模型有更高的识别精度，对检测特征较为接近的检测目标获得了更高的识别准确率，可以针对标注的非均质特征获得更强的识别效果。
3　充填料浆均质化检测
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充填料浆均质化识别会受到料浆浓度、灰砂比、搅拌时间、尾砂性质等多种因素的影响。本实验通过对非均质状态进行特征识别，判断料浆是否达到均质状态，料浆在不断搅拌直至均质状态的过程中，图中识别框以及准确度将逐步减少。为了验证模型对不同充填料浆识别的适用性，本文对不同料浆浓度、搅拌时间的充填料浆进行持续识别，判断优化后模型的适用性。
3.1　适用性分析
2组数据集的灰砂比均为1∶4，第1组数据集的料浆浓度为66%，搅拌时间分别为3 500、4 000、4 500 ms。第2组数据集的搅拌时间为4 000 ms，料浆浓度分别为40%、60%、80%。优化后模型识别效果如图10所示。在料浆浓度不变的情况下，随着搅拌时间的延长，料浆逐渐趋近于均质状态，非均质态特征的识别框减少，最终变为0，说明料浆达到了均质状态。
3.2　充填料浆均质化识别
在不同搅拌时间下对浓度分别为40%、60%和80%的充填料浆进行非均质特征识别，得到模型非均质特征识别准确率与搅拌时间的关系曲线，如图11所示。由图11可见，3组料浆的非均质特征识别准确率均随着搅拌时间增加呈下降趋势，且衰减形式符合指数函数规律。这表明随着搅拌的持续进行，料浆逐渐趋于均质化状态，非均质特征逐步减弱，非均质特征识别精度下降。
同时，不同浓度料浆的非均质特征识别准确率表现出一定差异。浓度40%的充填料浆会出现离析和分层现象，液态非均质特征识别精度更高，浓度80%的充填料浆黏稠度高，水泥尾砂不易分布均匀，固态非均质特征更明显。而浓度60%的充填料浆，通过搅拌较容易达到均质状态。通过拟合方程计算得出，在实际搅拌时间条件下，不同浓度料浆的识别准确率最大差异可达22.83%，进一步验证了料浆浓度对识别效果的显著影响。因此，可以认为料浆浓度是影响充填料浆均质化识别效果的重要因素之一。
随着搅拌时间延长，充填料浆逐渐由非均质状态过渡至均质状态，此时非均质特征值趋近于0。不同灰砂比条件下，其均质化进程存在一定差异。图12展示了不同灰砂比充填料浆在料浆浓度逐渐增加情况下，非均质特征值检测为0的时间变化曲线。拟合结果表明，3条曲线的决定系数（R2）分别为0.926、0.927和0.911，说明模型拟合效果良好。灰砂比1∶4、1∶6和1∶8对应的变化速率系数分别为0.502、0.493和0.447，表明不同灰砂比下的料浆均质化速率存在差异，但差异较小。这说明灰砂比对充填料浆的均质化过程有一定影响，但整体趋势一致，即随着搅拌时间增加，充填料浆最终均能达到较为均匀的状态。
将不同灰砂比和充填料浆浓度的识别结果进行敏感度分析，结果如图13所示。由图13可知，料浆浓度对非均质特征识别的影响最为显著。随着料浆浓度由40%逐渐增大至80%，非均质特征呈先下降后上升趋势，其中在50%～65%区间非均质特征识别稳定性最高。相比之下，灰砂比在相同料浆浓度条件下仅引起轻微波动，对非均质特征识别的影响明显小于料浆浓度。进一步结合颜色梯度可以发现，均质时间随着料浆浓度的升高呈现先增加后减弱的趋势，说明在合适的料浆浓度区间中，非均质特征较少。综上可见，料浆浓度是影响非均质识别的主要因素，其次是灰砂比，而延长搅拌时间在高料浆浓度条件下可显著提升充填料浆的均质化水平。
4　结论
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为解决充填料浆状态判断依赖人工、自动检测精度较低的问题，选择以Yolo-v7算法为基础，采用CBAM注意力模块和SPB-Conv小目标检测模块对Yolo-v7算法进行改进，并基于改进后的Yolo-v7算法对充填料浆均质化进行自动判断，得到了以下结论。
1）对充填料浆搅拌数据集进行算法消融试验，得出本实验算法平均准确率为75.3%，相较于改进前提高了17.5百分点，能够高效准确检测出充填料浆搅拌均质化以及充填料浆状态，对不同状态的充填料浆具有较高的识别准确率，在搅拌过程中具备所需的识别检测能力。
2）通过对质量分数40%、60%、80%，搅拌时间3 500、4 000、4 500 ms的料浆进行识别，认为改进后模型适用于识别不同状态下充填料浆均质化状态，且能保持75.3%的平均识别精准度。
3）通过料浆浓度和灰砂比敏感度分析可知，改变充填料浆料浆浓度和搅拌时间，都会对充填料浆非均质特征识别产生影响，充填料浆料浆浓度在50%～65%区间时非均质特征识别更稳定。
4）后续工作考虑增加专项识别模块和增强数据集方向，提高算法对充填料浆搅拌过程中变化状态下均质化和搅拌状态的识别，建立机器视觉与充填料浆均质化量化判断之间的联系。
基金
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  • 国家资助博士后研究人员计划(GZC20232063)
  • 陕西省自然科学基金项目(2024JC-YBQN-0507)
  • 陕西省博士后科研项目(2023BSHYDZZ142)
  • 陕西省创新能力支撑计划(2023-CX-TD-12)
文献
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2025年第45卷第5期
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doi: 10.3969/j.issn.0253-6099.2025.05.007
  • 接收时间：2025-04-20
  • 首发时间：2026-03-18
  • 出版时间：2025-10-01
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  • 收稿日期：2025-04-20
基金
国家资助博士后研究人员计划(GZC20232063)
陕西省自然科学基金项目(2024JC-YBQN-0507)
陕西省博士后科研项目(2023BSHYDZZ142)
陕西省创新能力支撑计划(2023-CX-TD-12)
作者信息
    西安建筑科技大学　资源工程学院，陕西　西安　710055
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https://castjournals.cast.org.cn/joweb/kygczz/CN/10.3969/j.issn.0253-6099.2025.05.007
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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