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Article(id=1251458153892757595, tenantId=1146029695717560320, journalId=1251194880429441115, issueId=1251458153020342360, articleNumber=null, orderNo=null, doi=10.3979/j.issn.1673-825X.202408150220, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1723651200000, receivedDateStr=2024-08-15, revisedDate=1748448000000, revisedDateStr=2025-05-29, acceptedDate=null, acceptedDateStr=null, onlineDate=1776300474855, onlineDateStr=2026-04-16, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1776300474855, onlineIssueDateStr=2026-04-16, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1776300474855, creator=13041195026, updateTime=1776300474855, updator=13041195026, issue=Issue{id=1251458153020342360, tenantId=1146029695717560320, journalId=1251194880429441115, year='2025', volume='37', issue='5', pageStart='627', pageEnd='780', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1776300474648, creator=13041195026, updateTime=1776311939434, updator=13041195026, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1251506239914586238, tenantId=1146029695717560320, journalId=1251194880429441115, issueId=1251458153020342360, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1251506239914586239, tenantId=1146029695717560320, journalId=1251194880429441115, issueId=1251458153020342360, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=677, endPage=687, ext={EN=ArticleExt(id=1251458154958110823, articleId=1251458153892757595, tenantId=1146029695717560320, journalId=1251194880429441115, language=EN, title=Classification of wood board laser speckle images based on deep learning, columnId=1251458154354131041, journalTitle=Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), columnName=Artificial Intelligenceand Big Data, runingTitle=null, highlight=null, articleAbstract=

This study proposes a wood board recognition method that integrates laser speckle technology with deep learning. Conventional photography and laser speckle imaging were employed to capture wood board images before and after modification treatments under both normal lighting and adverse conditions（including darkness and defocusing）. A corresponding dataset was then constructed. Classification experiments were conducted using the ResNet34 deep learning model. The results show that the ResNet34 model achieves high recognition accuracy when classifying laser speckle datasets and maintains good performance even under adverse environmental conditions. Furthermore, by introducing a convolutional block attention module（CBAM）to optimize the ResNet34 convolutional neural network, the classification accuracy for laser speckle images reached 93.29%. The combination of laser speckle technology and deep learning provides a low-environmental-requirement, efficient, and promising approach for wood board classification.

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提出一种激光散斑技术和深度学习技术相结合的木板识别方法。利用常规照相技术和激光散斑技术获取木板改性处理前后在常规照明和恶劣环境（黑暗环境、虚焦状态）下的木板图像及其激光散斑图像，并建立相应的数据集。采用ResNet34深度学习网络模型对数据集进行分类实验，结果表明，ResNet34模型对激光散斑数据集进行分类时能够达到较高的识别准确率，并在恶劣环境下，使用激光散斑图像数据集分类时，能够取得较好的分类效果。同时，引入混合注意力机制（convolutional block attention module，CBAM）模块对ResNet34卷积神经网络进行优化，用于激光散斑图像分类，识别正确率达到93.29%。利用激光散斑技术及深度学习模型进行木板种类识别对环境要求较低，是一种新颖的、有前途的高效技术方法。

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杜艳秋
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杜艳秋，副教授，硕士生导师，博士后，国家公派访问学者，主要研究方向为人工智能模式识别、微腔激光有源器件及传感、中红外晶体激光器技术等。E-mail: 。

李鑫，硕士，主要研究方向为人工智能模式识别。E-mail: 。

康辉，讲师，主要研究方向为人工智能模式识别、时频分析、图像处理。E-mail: 。

孙辉，讲师，主要研究方向为人工智能模式识别、图像处理。E-mail: 。

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杜艳秋，副教授，硕士生导师，博士后，国家公派访问学者，主要研究方向为人工智能模式识别、微腔激光有源器件及传感、中红外晶体激光器技术等。E-mail: 。

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杜艳秋，副教授，硕士生导师，博士后，国家公派访问学者，主要研究方向为人工智能模式识别、微腔激光有源器件及传感、中红外晶体激光器技术等。E-mail: 。

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李鑫，硕士，主要研究方向为人工智能模式识别。E-mail: 。

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李鑫，硕士，主要研究方向为人工智能模式识别。E-mail: 。

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康辉，讲师，主要研究方向为人工智能模式识别、时频分析、图像处理。E-mail: 。

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康辉，讲师，主要研究方向为人工智能模式识别、时频分析、图像处理。E-mail: 。

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孙辉，讲师，主要研究方向为人工智能模式识别、图像处理。E-mail: 。

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孙辉，讲师，主要研究方向为人工智能模式识别、图像处理。E-mail: 。

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Ecological Informatics, 2022 (69):101633., articleTitle=An effective and fast solution for classification of wood species: A deep transfer learning approach, refAbstract=null), Reference(id=1251458178651734796, tenantId=1146029695717560320, journalId=1251194880429441115, articleId=1251458153892757595, doi=null, pmid=null, pmcid=null, year=2023, volume=39, issue=4, pageStart=93, pageEnd=100, url=null, language=null, rfNumber=[32], rfOrder=38, authorNames=戴天虹, 翟冰, journalName=森林工程, refType=null, unstructuredReference=戴天虹,翟冰.基于改进EfficientNet的木材识别研究[J].森林工程, 2023, 39(4): 93-100., articleTitle=基于改进EfficientNet的木材识别研究, refAbstract=null), Reference(id=1251458178764981007, tenantId=1146029695717560320, journalId=1251194880429441115, articleId=1251458153892757595, doi=null, pmid=null, pmcid=null, year=2023, volume=39, issue=4, pageStart=93, pageEnd=100, url=null, language=null, rfNumber=[32], rfOrder=39, authorNames=DAI T H, ZHAI B, journalName=Forest Engineering, refType=null, unstructuredReference=DAI T H, ZHAI B. 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journalId=1251194880429441115, articleId=1251458153892757595, language=EN, label=Tab.1, caption=

Dataset related data

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采集条件常规照相图像激光散斑图像
原木板（未做改性处理）240240
改性处理①泡水处理200200
②铅笔涂画220220
③砂纸打磨200200
④粉刷白漆240240
⑤粉刷黑漆240240
10种木板合计13400①13400②
恶劣环境下黑暗环境下200200
虚焦状态下200200
10种木板合计40004000
所有总计17400③17400④
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数据集相关数据

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采集条件常规照相图像激光散斑图像
原木板（未做改性处理）240240
改性处理①泡水处理200200
②铅笔涂画220220
③砂纸打磨200200
④粉刷白漆240240
⑤粉刷黑漆240240
10种木板合计13400①13400②
恶劣环境下黑暗环境下200200
虚焦状态下200200
10种木板合计40004000
所有总计17400③17400④
), ArticleFig(id=1251458171366228572, tenantId=1146029695717560320, journalId=1251194880429441115, articleId=1251458153892757595, language=EN, label=Tab.2, caption=

Partition of training and testing sets

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数据集训练集测试集
激光散斑图像120601340
常规木板图像120601340
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训练集和测试集划分

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数据集训练集测试集
激光散斑图像120601340
常规木板图像120601340
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Division of training set and test set

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数据集训练集测试集
激光散斑图像156601740
常规木板图像156601740
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训练集和测试集划分

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数据集训练集测试集
激光散斑图像156601740
常规木板图像156601740
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Comparison of experimental effects

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图像采集环境数据集准确率/%
常规照明常规木板图像89.05
激光散斑图像90.13
恶劣环境常规木板图像85.30
激光散斑图像90.36
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实验效果对比

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图像采集环境数据集准确率/%
常规照明常规木板图像89.05
激光散斑图像90.13
恶劣环境常规木板图像85.30
激光散斑图像90.36
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Comparison of accuracy based on ResNet34 and ResNet34-CBAM

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模型（Model）准确率（Accuracy）/%
ResNet-CBAM93.29
ResNet3490.36
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ResNet34模型及ResNet34-CBAM模型的准确率对比

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模型（Model）准确率（Accuracy）/%
ResNet-CBAM93.29
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基于深度学习的木板激光散斑图像分类研究
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杜艳秋 , 李鑫 , 康辉 , 孙辉
重庆邮电大学学报（自然科学版） | 人工智能与大数据 2025,37(5): 677-687
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重庆邮电大学学报（自然科学版） | 人工智能与大数据 2025, 37(5): 677-687
基于深度学习的木板激光散斑图像分类研究
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杜艳秋 , 李鑫 , 康辉 , 孙辉
作者信息
  • 黑龙江科技大学 电子与信息工程学院，哈尔滨 150022

    • 杜艳秋，副教授，硕士生导师，博士后，国家公派访问学者，主要研究方向为人工智能模式识别、微腔激光有源器件及传感、中红外晶体激光器技术等。E-mail: 。

    李鑫，硕士，主要研究方向为人工智能模式识别。E-mail: 。

    康辉，讲师，主要研究方向为人工智能模式识别、时频分析、图像处理。E-mail: 。

    孙辉，讲师，主要研究方向为人工智能模式识别、图像处理。E-mail: 。

通讯作者:

Classification of wood board laser speckle images based on deep learning
Yanqiu DU , Xin LI , Hui KANG , Hui SUN
Affiliations
  • School of Electronic and Information Engineering, Heilongjiang University of Science and Technology, Harbin 150022, P R China
doi: 10.3979/j.issn.1673-825X.202408150220
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摘要
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提出一种激光散斑技术和深度学习技术相结合的木板识别方法。利用常规照相技术和激光散斑技术获取木板改性处理前后在常规照明和恶劣环境（黑暗环境、虚焦状态）下的木板图像及其激光散斑图像，并建立相应的数据集。采用ResNet34深度学习网络模型对数据集进行分类实验，结果表明，ResNet34模型对激光散斑数据集进行分类时能够达到较高的识别准确率，并在恶劣环境下，使用激光散斑图像数据集分类时，能够取得较好的分类效果。同时，引入混合注意力机制（convolutional block attention module，CBAM）模块对ResNet34卷积神经网络进行优化，用于激光散斑图像分类，识别正确率达到93.29%。利用激光散斑技术及深度学习模型进行木板种类识别对环境要求较低，是一种新颖的、有前途的高效技术方法。

激光散斑  /  ResNet34  /  图像分类  /  木板识别  /  深度学习

This study proposes a wood board recognition method that integrates laser speckle technology with deep learning. Conventional photography and laser speckle imaging were employed to capture wood board images before and after modification treatments under both normal lighting and adverse conditions（including darkness and defocusing）. A corresponding dataset was then constructed. Classification experiments were conducted using the ResNet34 deep learning model. The results show that the ResNet34 model achieves high recognition accuracy when classifying laser speckle datasets and maintains good performance even under adverse environmental conditions. Furthermore, by introducing a convolutional block attention module（CBAM）to optimize the ResNet34 convolutional neural network, the classification accuracy for laser speckle images reached 93.29%. The combination of laser speckle technology and deep learning provides a low-environmental-requirement, efficient, and promising approach for wood board classification.

laser speckle  /  ResNet34  /  image classification  /  wood board recognition  /  deep learning
杜艳秋, 李鑫, 康辉, 孙辉. 基于深度学习的木板激光散斑图像分类研究. 重庆邮电大学学报（自然科学版）, 2025 , 37 (5) : 677 -687 . DOI: 10.3979/j.issn.1673-825X.202408150220
Yanqiu DU, Xin LI, Hui KANG, Hui SUN. Classification of wood board laser speckle images based on deep learning[J]. Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition), 2025 , 37 (5) : 677 -687 . DOI: 10.3979/j.issn.1673-825X.202408150220
正文
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0　引言
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激光散斑是当激光穿过波长尺度上的粗糙物体或被物体反射后，由于光的强度分布被调制，从而形成的高对比度且尺寸细微的颗粒图样。在现代生物医学领域，利用激光穿过机体组织形成的激光散斑图像可以实现机体的非接触感知，能够无创、实时地监测脑血流量[1]、术中血流量[2]等血液微循环。在农业领域，利用激光散斑动力学可对农业害虫-蚜虫的摄食行为进行直接、非侵入式、遥感监测[3]，还可以对木瓜叶片的活性[4]，以及甜菜根须部分进行检测[5]，进而研究植物组织的动态变化，以便实时监测生长区的生理过程。另外，在传感及检测领域，利用激光散斑还可实现应力[6]、位移[7]、磁场[8]、振动[9]、粗糙度[10]等物理参量的无接触测量，以及结构中隐性损伤的可视化探测[11]。最近，激光散斑技术成功地用于图像分类识别领域，已经实现了黑暗环境下铜和不锈钢的识别[12]，以及煤矸石和煤的分类[13]。
木材作为我国国民经济建设中的重要自然资源，在国计民生中占据着重要的地位。对木材种类的鉴定和识别对于木材工业及贸易的规范化、濒危木材种类的保护、（古）建筑的保护及修复，以及犯罪学、考古学、生态学[14]等相关学科的发展具有重大的科学研究价值和实际应用意义。
传统的木材识别通常采用木材的颜色、光泽等物理特性和截面上的宏观解剖结构等信息，并依靠肉眼进行鉴别。在实验室中，木材识别通常是通过观察显微镜下木材薄片的各种解剖特征来进行。随着AI技术的迅猛发展，采用机器视觉和深度学习技术的分类模型及方法在木材识别领域拥有了前所未有的机会。2014年，Hafemann等[15]将卷积神经网络（convolutional neural networks，CNN）用于森林木材种类识别，对41种物种的树木横截面宏观图像和112种物种的微观图像数据进行分类，分别取得了95.77%和97.32%的准确率，表明CNN模型的分类性能优于基于纹理特征训练的模型。之后，基于卷积神经网络的深度学习模型广泛地应用于木材识别领域[14,16-17]。目前，利用深度学习的木材种类分类研究大部分是以木材的宏观表面图像[18-19]、宏观截面图像[20-21]、显微镜放大的微观解剖图像[22]建立数据集，部分文献采用近红外光谱图像[23-24]、高光谱图像[25]、x射线荧光光谱图像[26]等作为数据集，采用AlexNet[27]、VGG[28]、MobileNet[29]、ResNet[18,30]、InceptionNet、XceptionNet[31]、GoogLeNet、DenseNet、EfficientNet[32]、ShuffleNet等卷积神经网络[21]进行特征提取和分类。
相对于直接成像而言，形成激光散斑的光源为特定的激光，其高方向性和高单色性使其不仅适合于通常条件下的图像分类，而且特别适用于恶劣环境（如:夜间）中、及改性物体的远程监测及分类识别。木材的激光散斑图像是通过激光照射木材表面而形成的散斑图案，具有高分辨率特性，携带着木材表面独特的纹理特征，如年轮、裂纹、节子、颜色变化等，不但能够对木材进行高精度、无破坏性检测和识别，而且能够反映出木材的生长环境、处理工艺等信息。通过高分辨率的激光散斑图像，可以更加准确地识别木材种类、判断木材的质量和等级，进而确定其用途，为木材的合理利用、木材产业的可持续发展提供有力支持，尤其对珍贵木材或需要保持完整性的木材进行识别时，具有更加显著的优势。利用激光散斑技术，建立木材的激光散斑数据集，并利用深度学习技术进行训练和识别，可以实现更加高效、准确的木材识别，在木材产业的智能化、自动化和数字化方面具有重要意义。
目前，尚没有公开的木材激光散斑数据集，因此，本研究利用波长为632.8 nm的红色激光作为相干光源，建立不同条件下木材及改性木材的非散斑图像和激光散斑图像数据集，采用ResNet网络及优化的模型，对激光散斑技术在木材识别领域的应用进行探索性研究。
1　数据集的建立
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为了研究木板激光散斑数据集的分类效果，首先利用常规照相技术，建立木板图像数据集。然后，引入激光，获取木板的激光散斑图像，建立激光散斑数据集。
1.1　数据集图片采集系统
木材激光散斑图像采集系统如图1所示，该系统主要由激光器、扩束器、CMOS相机以及计算机组成。其中选用的激光器为氦氖激光器，波长为632.8 nm，功率为2 mW；扩束器中包含一个放大倍数为15倍扩束镜和一个准直透镜；计算机主要用来存储和显示CMOS相机传输的图像数据。在采集过程中，关闭激光器后，采集到的木板图像用于建立常规木板图像数据集。
1.2　图像采集及预处理
实验中选用10种常用家具木材作为样本，分别为北美红橡木、榉木、北美白蜡木、樱桃木、南美胡桃木、南美柚木、红花梨木、枫木、榉木和松木。每块木板加工为8 cm×8 cm×2 cm大小，每个材种的木板数量为20块。分别采集木板没有激光照射的图像以及激光散斑图像，每种木板各采集240张常规图像及激光散斑图像，对10种木材共采集木板常规图像和激光散斑图像各2400张，作为部分数据，分别用于构建数据集①和数据集②，相关信息如表1所示。作为示例，图2给出了一组相同条件下得到的10种木材的常规图像（第1、3行）及激光散斑图像（第2、4行）。
考虑到木板在运输、保存、加工等过程中可能受到的外界环境影响，人为对10种木板分别进行了如下改性处理:①泡水处理。每个品种的木板随机选取其中的几块进行泡水，泡水时间基本保持一致，大约为12 h，用来模拟被雨水淋湿的木板；②铅笔涂画处理。有些木板表面会有一些污渍，影响对木板的识别判断，将用铅笔涂画后的木板代替有污渍的木板；③砂纸打磨处理。用砂纸将木板表面打磨，使其表面变粗糙；④粉刷白漆。将砂纸打磨好的木板随机排列，粉刷油漆时使用油漆刷沿着一个方向进行刷漆，刷完一遍后放置3～4h待其晾干，再刷一遍油漆，重复此操作；⑤粉刷黑漆。与④操作步骤相同。
木板经过改性处理后，分别采集常规图像及激光散斑图像。改性处理①和改性处理③的每种木板以2种方式分别采集图像各200张；改性处理②的每种木板分别采集图像220张；改性处理④和改性处理⑤的木板分别采集图像各240张，这些图像构成数据集①和数据集②的另一部分，相关信息见表1。图3给出了白蜡木及改性后的常规图像和激光散斑图像。
同时，为了研究恶劣环境下利用激光散斑图像进行木板分类的优越性，分别在黑暗环境中和镜头虚焦状态下采集木板的常规图像及激光散斑图像，每种条件下每种木板分别采集200张常规照相图像和200张激光散斑图像，将常规图像和激光散斑图像分别添加至数据集①和数据集②中，构成数据集③和数据集④，相关信息列于表1。同样，以白蜡木为例，2种情况下采集到的常规图像和散斑图像如图4所示。
在图像的采集过程中，外部环境的不稳定性会对工业相机中的图像传感器造成干扰，同时CMOS相机中的电子电路噪声也会引入椒盐噪声，进而影响木板图像的质量。因此，在训练模型之前，必须对木板图像进行降噪处理，以符合CNN对输入图像的要求，并进一步提升CNN模型的泛化能力。本研究中选择中值滤波法对木板图像和激光散斑图像进行降噪处理，以提高后续对图像分析的准确性。中值滤波算法计算过程如图5所示。
其计算过程为首先将待处理像素的邻近区域内的像素值按照递增或递减的顺序排列，形成一个序列。确定这个序列的中位数，这个数便是实验所需的中值。然后，将这个中值作为新的像素值，用以替换原先待处理的像素值。最后，会在下一个待处理的像素上重复上述步骤，直至所有的像素点都被遍历。由于像素值的突变对中值滤波的影响较小，因此，中值滤波在处理激光散斑图像中的椒盐噪声时表现出色，能够有效地去除噪声，提升图像质量。图6为激光散斑图像去噪前后的示例。
1.3　图像数据集的建立
数据集相关信息见表1，最终共采集10种原木板（未做改性处理的木板）及其改性处理木板的常规图像和激光散斑图像各13400张，从而建立木板常规图像数据集①和木板激光散斑图像数据集②。将每一个数据集划分为2部分，其中90%作为训练集，共12060张图像；10%作为测试集，共1340张，详细信息如表2所示。
进一步，将恶劣环境下采集到的木板图像和激光散斑图像分别添加至已建立的数据集中形成新的数据集③和④，其中的木板常规图像和激光散斑图像分别达到17400张，分别用于对比研究在环境影响下的木板识别效果。将该数据集中图像的90%（共计15660张图像）作为训练集，剩余部分（共计1740张图像）为测试集，详细划分信息如表3所示。
2　网络架构及实验原理
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2.1　ResNet网络及优化
ResNet34网络是一种深度卷积神经网络模型，网络结构如图7所示[33]，依次包括7×7的卷积层，3×3的池化层、16个残差块（每个残差块中包含2个3×3的卷积层）、全局平均池化层及全连接层。该模型通过引入“残差块”（residual block）和跳跃链接方式（shortcut connections）来解决深层神经网络训练过程中的梯度消失和梯度爆炸问题，以便高效提取图像更多细节特征，从而实现更深层次的网络训练。同时，ResNet34网络需要的计算资源和训练时间较少，且ResNet34对硬件要求较低。ResNet网络的这些特性使其非常适合于对具有诸多细节特征的激光散斑图像进行分类识别处理。
为了进一步提高模型对激光散斑图像的分类能力，本文在ResNet34模型中引入混合注意力机制（convolutional block attention module，CBAM）模块进行改进和优化。CBAM模块[34]包括通道注意力模块和空间注意力模块，属于混合注意力机制，易于嵌入深度学习网络中，获取图像不同维度的特征信息，提高图像分类的能力。本研究中，在每个残差单元中嵌入混合注意力模块CBAM，构建了ResNetCBAM网络模型，强化网络在通道和空间上对细节特征的提取能力。优化后的ResNet34-CBAM网络模型如图8所示。首先，输入的激光散斑会通过卷积层进行初步的特征提取，然后，这些特征图会经过池化处理，以进一步减少数据的维度并增强特征的鲁棒性。接着，这些特征图会依次经过4种残差学习单元，这些单元能够有效地提取并整合激光散斑图像中的多层次特征。最后，网络会采用池化和全连接层对特征进行汇总和分类，从而实现对激光散斑图像的精确识别。
总之，ResNet34模型及其改进的ResNet-CBAM模型具有深层次的网络结构，并融合了混合注意力机制，能够更加关注激光散斑图像中的重要通道和区域，从而提高特征提取的准确性和效率，实现对激光散斑图像特征的全面捕捉和有效利用。并且，改进模型具有更强的泛化能力，保证了激光散斑图像识别的准确率。
2.2　激光散斑图像分类原理
激光散斑图的分类流程如图9所示，先利用卷积部分对激光散斑图执行特征提取操作，以获取图像中的关键信息，然后，这些提取的特征在全连接部分进一步地被处理和整合，进而生成分类的预测结果。接着，将预测结果与真实结果作为误差函数的输入，计算出一次迭代过程中的误差值，基于这一误差，优化算法会计算误差梯度，并据此对卷积神经网络的参数进行更新。最后，若误差降至预设的阈值以下或迭代次数达到设定的上限，则算法终止迭代过程；否则，将继续进行迭代并更新网络参数，直至满足停止的条件。
3　实验及结果讨论
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本文共开展了3组实验研究，首先采用ResNet模型对常规木板图像数据集和激光散斑数据集进行对比研究，进一步利用ResNet模型研究了在恶劣环境下对木板激光散斑图像的分类效果，然后利用优化的ResNet-CBAM模型对木材的激光散斑数据集进行分类实验。
3.1　常规图像及散斑图像的分类对比研究
本实验中使用了i5-12400 CPU和RTX 3070显卡，软件方面选择了PyTorch 1.8框架和Python 3.8编程语言，并在Windows操作系统上运行，选用了PyCharm作为开发环境。
采用ResNet34网络模型对常规木板图像和激光散斑数据集进行分类实验。实验中设定批量为32，学习率为0.001，并设定300个训练轮次，选择随机梯度下降（stochastic gradient descent，SGD）算法为优化器。
图10呈现了ResNet34网络在测试集上的准确率（Accuracy）的变化趋势；图11展示了损失函数（Loss）的变化曲线。从图10和图11中可以看出，ResNet34网络在木板图像数据集和激光散斑图像数据集上的测试效果较好，Loss曲线能较快收敛，网络模型在2个数据集上展现出了较高的准确度。ResNet34网络在激光散斑测试集上的准确率为90.13%，在木板图像的测试集上的准确率为89.05%，与模型在激光散斑图像上的识别准确率相比，大约低1.08%，相关数据见表3。实验结果说明，利用深度学习能够完成对激光散斑图像的分类，从而完成对木板材种的分类识别。
3.2　恶劣环境下的分类研究
进一步，使用ResNet34网络对恶劣环境下的数据集进行分类实验，损失函数变化曲线图和准确率变化曲线如图12—图13所示。可以看出，添加环境影响后，ResNet34网络在两个数据集上依然能取得较好的效果，Loss曲线能较快收敛，准确率变化曲线波动较小。虽然激光散斑图像测试集上的准确率变化曲线比木板图像测试集上的准确率变化曲线波动大，但是比不添加激光的木板图像分类准确率大约高5个百分点，其中测试集上木板图像的分类准确率为85.30%，激光散斑的图像分类准确率为90.36%，如表4所示。
与不添加环境影响的数据进行比较，恶劣环境下木板常规图像数据集的分类准确率由89.05%下降到85.30%；而在激光散斑图像数据中，分类准确率由90.13%略微上升到了90.36%，详细对比见表4。出现这种结果的原因是，不添加激光的木板图像数据集中，黑暗环境下以及虚焦状态下，图像质量受到影响，使图片的相关性变差，分类效果降低；而激光具有高度的方向性和亮度，使得散斑图像受外部环境影响较小，在黑暗环境中，仍能获取高质量的激光散斑图像。从图4可以看出，在虚焦状态下，激光散斑图像依然清晰。所以，在激光散斑数据集中添加环境影响后的图像质量较高，随着数据集中包含的数据增多，这有助于模型更好地学习和泛化。所以即使在恶劣环境下，模型在激光散斑图像上依然能保持较高的分类准确率。实验证明，使用激光散斑技术的木板识别过程不易受到外部环境变化的影响。
3.3　优化的网络模型分类研究
改进后的ResNet34-CBAM模型在激光散斑测试集上结果如图14—图15所示，可看出，准确率变化和损失函数曲线变化都较为稳定，改进的ResNet34网络Loss曲线收敛速度较快，网络在测试集上的准确率为93.29%，相比于ResNet34网络在测试集上的准确率提高了2.93%，对比结果如表5所示。实验结果表明，改进的ResNet34网络在添加环境影响后的测试集上仍有很高的准确率。
4　结束语
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本文提出了一种结合激光散斑技术和卷积神经网络的木板材种分类识别方法。实验中，采集了10种木板及改性后的常规图像以及激光散斑图像，建立了相应图像数据集。采用ResNet34网络对分别对常规照相获取的木板图像数据集和激光散斑数据集进行分类实验，其中在木板图像上的准确率为89.05%，在激光散斑图像上的准确率为90.13%，两者准确率相当，这说明利用激光散斑技术结合深度学习模型实现木材识别是可行的。另外，在相对较黑暗环境或虚焦状态下采集木板图像以及激光散斑图像，添加到数据集中，使用ResNet34网络模型再次进行分类实验，在木板图像数据集和激光散斑数据集的分类准确率分别为85.30%和90.36%。结果表明，使用激光散斑技术识别木板，对环境的要求较低。除此之外，利用残差单元中添加混合注意力机制CBAM模块，对ResNet34网络进行了优化，其在激光散斑数据集上的分类准确率为93.29%，达到较好的分类效果。本文将ResNet深度学习网络用于木板激光散斑数据集的分类研究，该方法为物体分类及自动识别提供了一条可行的、有前途的途径；进一步通过扩展数据集、改变卷积神经网络模型能够实现更多物体、更高精度的分类识别。
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2025年第37卷第5期
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doi: 10.3979/j.issn.1673-825X.202408150220
  • 接收时间：2024-08-15
  • 首发时间：2026-04-16
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  • 收稿日期：2024-08-15
  • 修回日期：2025-05-29
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    黑龙江科技大学 电子与信息工程学院，哈尔滨 150022

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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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