机器视觉检测综合设计实 手写数字识别 包括报告文档

资源内容介绍

本项目致力于开发和优化手写数字识别系统，利用机器视觉和深度学习技术，旨在提高识别准确率和处理效率。项目包含以下主要内容：数据集构建：数据集分为训练集和测试集，每个集分别包含大量手写数字图像，覆盖从0到9的所有数字。图像经过预处理，以确保数据的一致性和质量，为模型训练提供坚实基础。模型设计与训练：项目采用卷积神经网络（CNN）进行手写数字识别。CNN 模型通过多层卷积和池化操作，自动提取图像中的特征，并使用全连接层进行分类。在模型训练过程中，项目应用了多种数据增强技术，如旋转、缩放、平移等，以提高模型的泛化能力。为了优化模型性能，项目引入了交叉验证和超参数调优技术，确保模型在不同数据集上的稳定表现。性能评估与测试：使用独立的测试集对模型进行评估，通过混淆矩阵、准确率、召回率、F1 分数等多种指标全面评估模型的性能。项目还进行了一系列对比实验，分析不同模型架构、超参数设置对识别效果的影响，找出最优配置。

<link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/base.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/css/fancy.min.css" rel="stylesheet"/><link href="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89617211/raw.css" rel="stylesheet"/><div id="sidebar" style="display: none"><div id="outline"></div></div><div class="pf w0 h0" data-page-no="1" id="pf1"><div class="pc pc1 w0 h0"><img alt="" class="bi x0 y0 w1 h1" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89617211/bg1.jpg"/><div class="c x1 y1 w2 h2"><div class="t m0 x0 h3 y2 ff1 fs0 fc0 sc0 ls0 ws0">1</div></div><div class="t m0 x2 h4 y3 ff1 fs1 fc0 sc0 ls0 ws0"> </div><div class="t m0 x3 h5 y4 ff2 fs2 fc0 sc1 ls0 ws0">《机器<span class="_ _0"></span>视觉检<span class="_ _0"></span>测综合<span class="_ _0"></span>设计实<span class="_ _0"></span>验》</div><div class="t m0 x4 h6 y5 ff3 fs3 fc0 sc0 ls0 ws0"> </div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div><div id="pf2" class="pf w0 h0" data-page-no="2"><div class="pc pc2 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89617211/bg2.jpg"><div class="c x1 y1 w2 h2"><div class="t m0 x0 h3 y2 ff1 fs0 fc0 sc0 ls0 ws0">2</div></div><div class="t m0 x4 h7 y6 ff4 fs4 fc0 sc0 ls0 ws0">&#19968;<span class="_ _0"></span>&#12289;<span class="_ _0"></span>&#20171;<span class="_ _0"></span>&#32461;<span class="_ _0"></span>&#20027;<span class="_ _1"></span>&#25104;<span class="_ _0"></span>&#20998;<span class="_ _0"></span>&#20998;<span class="_ _0"></span>&#26512;<span class="_ _0"></span>&#65288;<span class="_ _1"></span><span class="ff1">principal <span class="_ _0"></span>component <span class="_ _0"></span>analysis, <span class="_ _0"></span>PCA<span class="_ _2"></span></span>&#65289;<span class="_ _0"></span>&#26041;<span class="_ _0"></span>&#27861;<span class="_ _0"></span>&#30340;<span class="_ _0"></span>&#21407;</div><div class="t m0 x4 h7 y7 ff4 fs4 fc0 sc0 ls0 ws0">&#29702;&#21450;&#20854;&#22312;&#22270;&#20687;&#35782;&#21035;&#24212;&#29992;&#20013;&#30340;&#20855;&#20307;&#27493;&#39588;&#12290;<span class="_ _3"></span>&#65288;<span class="ff1">20<span class="_"> </span></span>&#20998;&#65289;</div><div class="t m0 x4 h7 y8 ff4 fs4 fc0 sc0 ls0 ws0">&#20108;&#12289;<span class="_ _0"></span>&#25551;<span class="_ _0"></span>&#36848;<span class="_ _4"> </span><span class="ff1">k<span class="_"> </span></span>&#36817;<span class="_ _0"></span>&#37051;<span class="_ _0"></span>&#65288;<span class="ff1">k-Nearest <span class="_ _0"></span>Neighbor, <span class="_ _0"></span>kNN</span>&#65289;<span class="_ _0"></span>&#31639;<span class="_ _0"></span>&#27861;&#30340;<span class="_ _0"></span>&#24605;<span class="_ _0"></span>&#24819;&#21450;<span class="_ _0"></span>&#27493;<span class="_ _0"></span>&#39588;&#65292;<span class="_ _0"></span>&#24182;</div><div class="t m0 x4 h7 y9 ff4 fs4 fc0 sc0 ls0 ws0">&#25351;&#20986;&#23427;&#30340;&#20248;&#32570;&#28857;&#12290;<span class="_ _3"></span>&#65288;<span class="ff1">15<span class="_"> </span></span>&#20998;&#65289;</div><div class="t m0 x4 h7 ya ff4 fs4 fc0 sc0 ls0 ws0">&#19977;&#12289;<span class="ff1">digit_dataset<span class="_"> </span></span>&#20013;&#26159;&#23454;&#39564;&#29992;&#30340;&#25163;&#20889;&#20307;&#25968;&#23383;&#22270;&#20687;&#65288;<span class="ff1">0-9</span>&#65292;<span class="_ _5"></span>&#20849;<span class="_ _4"> </span><span class="ff1">10<span class="_"> </span></span>&#31867;&#65289;<span class="_ _3"></span>&#65292;<span class="_ _5"></span>&#20854;</div><div class="t m0 x4 h7 yb ff4 fs4 fc0 sc0 ls0 ws0">&#20013;<span class="_ _4"> </span><span class="ff1">train<span class="_"> </span></span>&#25991;&#20214;&#22841;&#26159;&#35757;&#32451;&#26679;&#26412;&#22270;&#20687;&#65292;&#27599;&#20010;&#31867;&#21035;<span class="_ _6"> </span><span class="ff1">5<span class="_"> </span></span>&#24133;<span class="_ _7"></span>&#65307;<span class="_ _7"></span><span class="ff1">test<span class="_"> </span><span class="ff4">&#25991;&#20214;&#22841;&#26159;&#27979;&#35797;&#26679;</span></span></div><div class="t m0 x4 h7 yc ff4 fs4 fc0 sc0 ls0 ws0">&#26412;&#22270;&#20687;&#65292;&#27599;&#31867;<span class="_ _6"> </span><span class="ff1">3<span class="_"> </span></span>&#24133;&#12290;&#35757;&#32451;&#21644;&#27979;&#35797;&#22270;&#20687;&#30340;&#22823;&#23567;&#22343;&#20026;<span class="_ _4"> </span><span class="ff1">50*50<span class="_"> </span></span>&#20687;&#32032;&#12290;</div><div class="t m0 x4 h7 yd ff1 fs4 fc0 sc0 ls0 ws0">1. <span class="_"> </span><span class="ff4">&#30452;<span class="_ _0"></span>&#25509;&#20351;<span class="_ _0"></span>&#29992;&#21407;<span class="_ _0"></span>&#22987;&#35757;<span class="_ _0"></span>&#32451;&#21644;<span class="_ _0"></span>&#27979;&#35797;<span class="_ _0"></span>&#26679;&#26412;<span class="_ _0"></span>&#22270;&#20687;<span class="_ _0"></span>&#65292;&#21033;<span class="_ _0"></span>&#29992;&#26368;<span class="_ _0"></span>&#36817;&#37051;<span class="_ _0"></span>&#20998;&#31867;<span class="_ _0"></span>&#22120;&#65288;<span class="_ _0"></span></span>NNC<span class="ff4">&#65289;</span></div><div class="t m0 x4 h7 ye ff4 fs4 fc0 sc0 ls0 ws0">&#23545;&#27979;&#35797;&#26679;&#26412;&#36827;&#34892;&#20998;&#31867;&#65292;&#35745;&#31639;&#20986;&#27491;&#30830;&#35782;&#21035;&#29575;&#12290;<span class="_ _3"></span>&#65288;<span class="ff1">25<span class="_"> </span></span>&#20998;&#65289;</div><div class="t m0 x4 h8 yf ff2 fs5 fc1 sc2 ls0 ws0">&#20195;&#30721;&#65306;</div><div class="t m0 x4 h8 y10 ff1 fs5 fc0 sc0 ls0 ws0">% <span class="_"> </span><span class="ff4">&#35774;&#32622;&#35757;&#32451;&#21644;&#27979;&#35797;&#25968;&#25454;&#38598;&#30340;&#25991;&#20214;&#36335;&#24452;</span></div><div class="t m0 x4 h8 y11 ff1 fs5 fc0 sc0 ls0 ws0">trainPath = 'D:\zuomian\32\<span class="ff4">&#23454;&#39564;<span class="_ _8"> </span></span>8\digit_dataset\train';</div><div class="t m0 x4 h8 y12 ff1 fs5 fc0 sc0 ls0 ws0">testPath = 'D:\zuomian\32\<span class="ff4">&#23454;&#39564;<span class="_ _8"> </span></span>8\digit_dataset\test';</div><div class="t m0 x4 h8 y13 ff1 fs5 fc0 sc0 ls0 ws0">% <span class="_"> </span><span class="ff4">&#35835;&#21462;&#35757;&#32451;&#38598;</span></div><div class="t m0 x4 h9 y14 ff1 fs5 fc0 sc0 ls0 ws0">trainData = [];</div><div class="t m0 x4 h9 y15 ff1 fs5 fc0 sc0 ls0 ws0">trainLabels = [];</div><div class="t m0 x4 h8 y16 ff1 fs5 fc0 sc0 ls0 ws0">trainFiles = dir(fullfile(trainPath, '*.jpg')); % <span class="_"> </span><span class="ff4">&#33719;&#21462;&#25152;&#26377;&#22270;&#20687;&#25991;&#20214;</span></div><div class="t m0 x4 h8 y17 ff1 fs5 fc0 sc0 ls0 ws0">fprintf('<span class="ff4">&#35757;&#32451;&#38598;&#21253;&#21547;</span> <span class="_"> </span>%d <span class="_"> </span><span class="ff4">&#24352;&#22270;&#20687;</span>\n', length(trainFiles));</div><div class="t m0 x4 h9 y18 ff1 fs5 fc0 sc0 ls0 ws0">for i = 1:length(trainFiles)</div><div class="t m0 x4 h9 y19 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>filename = trainFiles(i).name;</div><div class="t m0 x4 h9 y1a ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>img = imread(fullfile(trainPath, filename));</div><div class="t m0 x4 h9 y1b ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>if size(img, 3) == 3</div><div class="t m0 x4 h8 y1c ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _a"> </span>img = rgb2gray(img); % <span class="_"> </span><span class="ff4">&#36716;&#25442;&#20026;&#28784;&#24230;&#22270;&#20687;</span></div><div class="t m0 x4 h9 y1d ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>end</div><div class="t m0 x4 h8 y1e ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>img = imresize(img, [50 50]); % <span class="_"> </span><span class="ff4">&#30830;&#20445;&#22270;&#20687;&#22823;&#23567;&#20026;<span class="_ _8"> </span></span>50x50</div><div class="t m0 x4 h8 y1f ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>img = double(img(:)'); % <span class="_"> </span><span class="ff4">&#23637;&#24179;&#24182;&#36716;&#25442;&#20026;<span class="_ _8"> </span></span>double<span class="_"> </span><span class="ff4">&#31867;&#22411;</span></div><div class="t m0 x4 h9 y20 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>trainData = [trainData; img];</div><div class="t m0 x4 h8 y21 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>label = str2double(filename(4)); <span class="_ _0"></span>% <span class="_"> </span><span class="ff4">&#25552;&#21462;<span class="_ _0"></span>&#25991;&#20214;&#21517;<span class="_ _0"></span>&#20013;&#30340;&#31867;<span class="_ _0"></span>&#21035;&#65288;&#31532;<span class="_ _6"> </span></span>4<span class="_"> </span><span class="ff4">&#20010;&#23383;<span class="_ _0"></span>&#31526;&#34920;<span class="_ _0"></span>&#31034;&#31867;</span></div><div class="t m0 x4 h8 y22 ff4 fs5 fc0 sc0 ls0 ws0">&#21035;&#65289;</div><div class="t m0 x4 h9 y23 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>trainLabels = [trainLabels; label];</div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div><div id="pf3" class="pf w0 h0" data-page-no="3"><div class="pc pc3 w0 h0"><img class="bi x0 y0 w1 h1" alt="" src="/image.php?url=https://csdnimg.cn/release/download_crawler_static/89617211/bg3.jpg"><div class="c x1 y1 w2 h2"><div class="t m0 x0 h3 y2 ff1 fs0 fc0 sc0 ls0 ws0">3</div></div><div class="t m0 x4 h9 y24 ff1 fs5 fc0 sc0 ls0 ws0">end</div><div class="t m0 x4 h8 y25 ff1 fs5 fc0 sc0 ls0 ws0">% <span class="_"> </span><span class="ff4">&#23558;&#35757;&#32451;&#26631;&#31614;&#36716;&#25442;&#20026;&#20998;&#31867;&#25968;&#32452;&#65292;&#24182;&#25351;&#23450;&#31867;&#21035;&#39034;&#24207;</span></div><div class="t m0 x4 h9 y26 ff1 fs5 fc0 sc0 ls0 ws0">trainLabels = categorical(trainLabels, 0:9);</div><div class="t m0 x4 h8 y27 ff1 fs5 fc0 sc0 ls0 ws0">% <span class="_"> </span><span class="ff4">&#35835;&#21462;&#27979;&#35797;&#38598;</span></div><div class="t m0 x4 h9 y28 ff1 fs5 fc0 sc0 ls0 ws0">testData = [];</div><div class="t m0 x4 h9 y29 ff1 fs5 fc0 sc0 ls0 ws0">testLabels = [];</div><div class="t m0 x4 h8 y2a ff1 fs5 fc0 sc0 ls0 ws0">testFiles = dir(fullfile(testPath, '*.bmp')); % <span class="_"> </span><span class="ff4">&#33719;&#21462;&#25152;&#26377;&#22270;&#20687;&#25991;&#20214;</span></div><div class="t m0 x4 h8 y2b ff1 fs5 fc0 sc0 ls0 ws0">fprintf('<span class="ff4">&#27979;&#35797;&#38598;&#21253;&#21547;</span> <span class="_"> </span>%d <span class="_"> </span><span class="ff4">&#24352;&#22270;&#20687;</span>\n', length(testFiles));</div><div class="t m0 x4 h9 y2c ff1 fs5 fc0 sc0 ls0 ws0">for i = 1:length(testFiles)</div><div class="t m0 x4 h9 y2d ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>filename = testFiles(i).name;</div><div class="t m0 x4 h9 y2e ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>filepath = fullfile(testPath, filename);</div><div class="t m0 x4 h8 y2f ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>fprintf('<span class="ff4">&#35835;&#21462;&#27979;&#35797;&#22270;&#20687;</span>: %s\n', filepath);</div><div class="t m0 x4 h9 y30 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>img = imread(filepath);</div><div class="t m0 x4 h9 y31 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>if size(img, 3) == 3</div><div class="t m0 x4 h8 y32 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _a"> </span>img = rgb2gray(img); % <span class="_"> </span><span class="ff4">&#36716;&#25442;&#20026;&#28784;&#24230;&#22270;&#20687;</span></div><div class="t m0 x4 h9 y33 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>end</div><div class="t m0 x4 h8 yf ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>img = imresize(img, [50 50]); % <span class="_"> </span><span class="ff4">&#30830;&#20445;&#22270;&#20687;&#22823;&#23567;&#20026;<span class="_ _8"> </span></span>50x50</div><div class="t m0 x4 h8 y34 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>img = double(img(:)'); % <span class="_"> </span><span class="ff4">&#23637;&#24179;&#24182;&#36716;&#25442;&#20026;<span class="_ _8"> </span></span>double<span class="_"> </span><span class="ff4">&#31867;&#22411;</span></div><div class="t m0 x4 h9 y35 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>testData = [testData; img];</div><div class="t m0 x4 h8 y10 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>label = str2double(filename(4)); <span class="_ _0"></span>% <span class="_"> </span><span class="ff4">&#25552;&#21462;<span class="_ _0"></span>&#25991;&#20214;&#21517;<span class="_ _0"></span>&#20013;&#30340;&#31867;<span class="_ _0"></span>&#21035;&#65288;&#31532;<span class="_ _6"> </span></span>4<span class="_"> </span><span class="ff4">&#20010;&#23383;<span class="_ _0"></span>&#31526;&#34920;<span class="_ _0"></span>&#31034;&#31867;</span></div><div class="t m0 x4 h8 y36 ff4 fs5 fc0 sc0 ls0 ws0">&#21035;&#65289;</div><div class="t m0 x4 h9 y37 ff1 fs5 fc0 sc0 ls0 ws0"> <span class="_ _9"> </span>testLabels = [testLabels; label];</div><div class="t m0 x4 h9 y38 ff1 fs5 fc0 sc0 ls0 ws0">end</div><div class="t m0 x4 h8 y16 ff1 fs5 fc0 sc0 ls0 ws0">% <span class="_"> </span><span class="ff4">&#23558;&#27979;&#35797;&#26631;&#31614;&#36716;&#25442;&#20026;&#20998;&#31867;&#25968;&#32452;&#65292;&#24182;&#25351;&#23450;&#31867;&#21035;&#39034;&#24207;</span></div><div class="t m0 x4 h9 y39 ff1 fs5 fc0 sc0 ls0 ws0">testLabels = categorical(testLabels, 0:9);</div><div class="t m0 x4 h8 y3a ff1 fs5 fc0 sc0 ls0 ws0">% <span class="_"> </span><span class="ff4">&#20351;&#29992;&#26368;&#36817;&#37051;&#20998;&#31867;&#22120;&#36827;&#34892;&#20998;&#31867;</span></div><div class="t m0 x4 h9 y1a ff1 fs5 fc0 sc0 ls0 ws0">Mdl = fitcknn(trainData, trainLabels, 'NumNeighbors', 1);</div><div class="t m0 x4 h8 y1c ff1 fs5 fc0 sc0 ls0 ws0">% <span class="_"> </span><span class="ff4">&#36827;&#34892;&#39044;&#27979;</span></div><div class="t m0 x4 h9 y1d ff1 fs5 fc0 sc0 ls0 ws0">predictedLabels = predict(Mdl, testData);</div><div class="t m0 x4 h8 y1f ff1 fs5 fc0 sc0 ls0 ws0">% <span class="_"> </span><span class="ff4">&#35745;&#31639;&#27491;&#30830;&#35782;&#21035;&#29575;</span></div><div class="t m0 x4 h9 y20 ff1 fs5 fc0 sc0 ls0 ws0">accuracy = sum(predictedLabels == testLabels) / length(testLabels);</div><div class="t m0 x4 h8 y21 ff1 fs5 fc0 sc0 ls0 ws0">fprintf('<span class="ff4">&#27491;&#30830;&#35782;&#21035;&#29575;</span>: %.2f%%\n', accuracy * 100);</div><div class="t m0 x4 h8 y3b ff1 fs5 fc0 sc0 ls0 ws0">% <span class="_"> </span><span class="ff4">&#26174;&#31034;&#32467;&#26524;</span></div></div><div class="pi" data-data='{"ctm":[1.611830,0.000000,0.000000,1.611830,0.000000,0.000000]}'></div></div>