基于MATLAB的高速公路裂缝检测方案

一、系统架构设计

% 主程序框架
[preprocessed, mask] = preprocessImage(rawImage); % 图像预处理
edges = detectCrackEdges(preprocessed); % 裂缝边缘检测
regions = extractCrackRegions(edges, mask); % 裂缝区域提取
features = computeFractalFeatures(regions); % 分形特征计算
labels = classifyCracks(features); % 裂缝分类
visualizeResults(rawImage, regions, labels); % 结果可视化

二、核心

1. 图像预处理

function [processed, mask] = preprocessImage(img)
    % 转换为灰度图像
    gray = rgb2gray(img);

    % 自适应直方图均衡化
    enhanced = adapthisteq(gray);

    % 多尺度分解去噪
    [low, high] = wmsfilter(enhanced, 'db3', 3);
    denoised = low + 0.5*high;

    % 生成路面掩膜
    [mask, ~] = segmentRoad(denoised);

    processed = imadjust(denoised, stretchlim(denoised), []);
end

2. 裂缝边缘检测

function edges = detectCrackEdges(img)
    % 改进Canny算法参数
    sigma = 1.5;
    lowThresh = 0.1;
    highThresh = 0.3;

    % 方向梯度计算
    [Gx, Gy] = imgradientxy(img, 'sobel');
    gradMag = sqrt(Gx.^2 + Gy.^2);
    gradDir = atan2d(Gy, Gx);

    % 非极大值抑制
    suppressed = nonmaxsuppression(gradMag, gradDir);

    % 双阈值检测
    edges = edge(img, 'canny', [lowThresh highThresh], sigma);

    % 边缘连接优化
    edges = bwareaopen(edges, 50);
    edges = imclose(edges, strel('disk',3));
end

3. 裂缝区域提取

function regions = extractCrackRegions(edges, roadMask)
    % 形态学操作
    se = strel('diamond',2);
    opened = imopen(edges, se);
    closed = imclose(opened, se);

    % 区域生长算法
    markers = imextendedmin(closed, 0.2);
    L = watershed(closed);

    % 提取连通区域
    stats = regionprops(L, 'Area', 'Centroid', 'BoundingBox');
    regions = struct('BoundingBox', [], 'Area', []);

    for i = 1:numel(stats)
        if stats(i).Area > 500 && ismember(L(i), unique(L(roadMask==1)))
            regions(end+1) = stats(i);
        end
    end
end

4. 分形特征计算

function features = computeFractalFeatures(regions)
    features = struct('FractalDim', [], 'Length', [], 'Width', []);

    for i = 1:numel(regions)
        % 计算分形维数
        fd = computeFractalDimension(regions(i).BoundingBox);

        % 计算裂缝尺寸
        stats = regionprops(regions(i).BoundingBox, 'MajorAxisLength', 'MinorAxisLength');

        features(i).FractalDim = fd;
        features(i).Length = stats.MajorAxisLength;
        features(i).Width = stats.MinorAxisLength;
    end
end

function fd = computeFractalDimension(bbox)
    % 基于盒计数法
    img = imcrop(imread('road_crack.jpg'), bbox);
    [counts, ~] = imhist(img);
    p = counts / numel(img);

    % 线性回归计算分形维数
    log_counts = log10(counts(find(p>0)));
    log_scale = log10(1./find(p>0));
    p = polyfit(log_scale, log_counts, 1);
    fd = -p(1);
end

三、深度学习增强模块

1. 轻量化CNN模型

layers = [
    imageInputLayer([256 256 3])
    convolution2dLayer(3, 16, 'Padding', 'same')
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)

    convolution2dLayer(3, 32, 'Padding', 'same')
    reluLayer
    maxPooling2dLayer(2, 'Stride', 2)

    fullyConnectedLayer(64)
    reluLayer
    dropoutLayer(0.5)

    fullyConnectedLayer(3) % 3类：细缝/宽缝/网裂
    softmaxLayer
    classificationLayer];

2. 迁移学习实现

% 加载预训练模型
net = alexnet;

% 修改最后三层
layers = net.Layers;
layers(end-2) = convolution2dLayer(1, 3, 'Name', 'new_conv');
layers(end-1) = reluLayer('Name', 'new_relu');
layers(end) = classificationLayer('Name', 'new_classoutput');

% 数据增强
augmenter = imageDataAugmenter('RandRotation', [-10 10], ...
    'RandXReflection', true, 'RandYReflection', true);

% 训练选项
options = trainingOptions('adam',...
    'MaxEpochs', 20,...
    'MiniBatchSize', 32,...
    'InitialLearnRate', 0.001,...
    'Shuffle', 'every-epoch',...
    'ValidationData', validationData,...
    'ValidationFrequency', 30);

四、工程化优化策略

1. 多尺度处理

function multiScaleFeatures = computeMultiScale(img)
    scales = [1, 0.5, 0.25];
    features = cell(1,numel(scales));

    for i = 1:numel(scales)
        scaledImg = imresize(img, scales(i));
        features{
   i} = extractHOGFeatures(scaledImg);
    end

    multiScaleFeatures = cat(2, features{
   :});
end

2. GPU加速

% 启用GPU计算
gpuImg = gpuArray(img);
gpuEdges = edge(gpuImg, 'canny');

% 并行区域提取
parfor i = 1:numel(regions)
    regionFeatures(i) = extractFeatures(regions(i));
end

推荐代码 matlab高速公路的裂缝进行检测 www.youwenfan.com/contentald/53261.html

五、结果可视化与报告

function visualizeResults(img, regions, labels)
    figure;
    imshow(img);
    hold on;

    colors = lines(numel(unique(labels)));

    for i = 1:numel(regions)
        % 绘制边界框
        rectangle('Position', regions(i).BoundingBox, ...
            'EdgeColor', colors(labels(i),:), 'LineWidth', 2);

        % 显示分类结果
        text(regions(i).BoundingBox(1), regions(i).BoundingBox(2)-10, ...
            sprintf('Class: %d', labels(i)), ...
            'Color', colors(labels(i),:), 'FontSize', 12);
    end

    % 生成检测报告
    report = struct(...
        'TotalCracks', numel(regions),...
        'ClassDistribution', histcounts(labels),...
        'AvgLength', mean([regions.Length]));

    disp('检测报告:');
    disp(report);
end

六、典型应用场景

1. 施工期质量检测
   • 检测新铺路面的早期裂缝
   • 自动标记裂缝位置与类型
2. 养护期病害管理
   • 定期巡检裂缝发展情况
   • 生成维修优先级热力图
3. 交通事故分析
   • 识别撞击导致的放射状裂缝
   • 重建事故车辆行驶轨迹