基于matlab交通限速标志识别系统【源码51期】-育师

一、项目简介

本系统基于MATLAB图像处理与计算机视觉工具箱，设计并实现了一个针对道路交通限速标志的自动识别系统。系统包含两大核心模块：核心识别函数（Mat5_2rec.m）实现了完整的识别流程——将输入图像转换至HSV色彩空间，利用红色阈值分割提取标志区域，通过形态学操作去除噪声并填充空洞，基于区域周长和面积计算圆形度以精确定位圆形标志，随后裁剪标志区域并进行二值化增强，最后调用OCR函数识别其中的数字；图形用户界面模块（Mat5_2.m与Mat5_2.fig）基于GUIDE框架开发，用户可通过按钮依次执行“载入图像”、“预处理”（显示颜色分割结果）、“标志定位”（显示轮廓与圆形度度量）、“数字分割”（显示二值化图像）及“结果展示”（在原图上标记标志并弹窗显示识别结果）等步骤，实现交互式操作。

二、部分源码

function varargout = Mat5_2(varargin)

% Begin initialization code - DO NOT EDIT

gui_Singleton = 1;

gui_State = struct('gui_Name', mfilename, ...

'gui_Singleton', gui_Singleton, ...

'gui_OpeningFcn', @Mat5_2_OpeningFcn, ...

'gui_OutputFcn', @Mat5_2_OutputFcn, ...

'gui_LayoutFcn', [] , ...

'gui_Callback', []);

if nargin && ischar(varargin{1})

gui_State.gui_Callback = str2func(varargin{1});

end

if nargout

[varargout{1:nargout}] = gui_mainfcn(gui_State, varargin{:});

else

gui_mainfcn(gui_State, varargin{:});

end

% End initialization code - DO NOT EDIT

% --- Executes just before Mat5_2 is made visible.

function Mat5_2_OpeningFcn(hObject, eventdata, handles, varargin)

handles.output = hObject;

% Update handles structure

guidata(hObject, handles);

% --- Outputs from this function are returned to the command line.

function varargout = Mat5_2_OutputFcn(hObject, eventdata, handles)

% Get default command line output from handles structure

varargout{1} = handles.output;

% --- Executes on button press in pushbutton1.

function pushbutton1_Callback(hObject, eventdata, handles)%载入图像

global Image

[filename,pathname]=uigetfile({'*.jpg';'*.bmp';'.gif'},'选择图片');

str=[pathname filename];

Image1 = imread(str);

Image=Image1;

axes(handles.axes1);

imshow(Image1);

function edit1_Callback(hObject, eventdata, handles)

% --- Executes during object creation, after setting all properties.

function edit1_CreateFcn(hObject, eventdata, handles)

if ispc && isequal(get(hObject,'BackgroundColor'), get(0,'defaultUicontrolBackgroundColor'))

set(hObject,'BackgroundColor','white');

end

% --- Executes on button press in pushbutton2.

function pushbutton2_Callback(hObject, eventdata, handles)%预处理

global Image

[~,~,hsvReg1,~,~,~]=Mat5_2rec(Image);

axes(handles.axes2); %指定需要清空的坐标轴

cla reset;

axes(handles.axes2);

imshow(hsvReg1);

% --- Executes on button press in pushbutton3.

function pushbutton3_Callback(hObject, eventdata, handles)%标志定位

% hObject handle to pushbutton3 (see GCBO)

% eventdata reserved - to be defined in a future version of MATLAB

% handles structure with handles and user data (see GUIDATA)

global Image

axes(handles.axes2); %指定需要清空的坐标轴

cla reset;

[~,~,~,B,L,~]=Mat5_2rec(Image);

axes(handles.axes2);

imshow(label2rgb(L,@jet,[.5 .5 .5]));

hold on;

for k=1:length(B)

boundary=B{k};

plot(boundary(:,2),boundary(:,1),'w','LineWidth',2);

end

stats=regionprops(L,'Area','Centroid');

threshold=0.85;

for k=1:length(B)

boundary=B{k};

delta_sq=diff(boundary).^2;

perimeter=sum(sqrt(sum(delta_sq,2)));

area=stats(k).Area;

metric=4*pi*area/perimeter^2;

metric_string=sprintf('%2.2f',metric);

if metric>threshold

centroid=stats(k).Centroid;

plot(centroid(1),centroid(2),'ko');

r=perimeter/(2*pi);

end

rect=[centroid(1)-r*sqrt(2)/2.5 centroid(2)-r*sqrt(2)/2.5 r*sqrt(2)/1.25 r*sqrt(2)/1.25];

rectangle('Position',rect,'LineWidth',4,'EdgeColor','g');

text(boundary(1,2)-35,boundary(1,1)+13,metric_string,'Color','y','FontSize',14,'FontWeight','bold');

end

% --- Executes on button press in pushbutton4.

function pushbutton4_Callback(hObject, eventdata, handles)%数字分割

global Image

axes(handles.axes2); %指定需要清空的坐标轴

cla reset;

[~,~,~,~,~,BW]=Mat5_2rec(Image);

axes(handles.axes2);

imshow(BW);

% --- Executes on button press in pushbutton5.

function pushbutton5_Callback(hObject, eventdata, handles)%结果展示

global Image

axes(handles.axes2); %指定需要清空的坐标轴

cla reset;

[number,rect,~,~,~,~]=Mat5_2rec(Image);

str1='限速';

str2=num2str(number);

numstr=[str1,str2];

axes(handles.axes2);

imshow(Image);

rectangle('Position',rect,'LineWidth',4,'EdgeColor','g');

result=strcat('标志识别：',string(numstr),'');

msgbox(result,'识别结果','warn')

set(handles.edit1,'String',num2str(numstr));

三、运行结果

四、总结

该系统能够有效识别红色圆形限速标志中的数字信息，HSV颜色分割策略对红色目标具有良好的提取能力，圆形度判定保证了标志定位的准确性，形态学处理增强了区域完整性，OCR识别实现了数字内容的读取。界面分步设计有助于用户理解各处理环节，适用于交通标志识别的研究演示与教学实验场景。后续可引入更多颜色和形状的识别能力以覆盖更广泛的标志类型，或结合深度学习方法提升OCR在复杂环境下的鲁棒性，进一步增强系统的实用价值。