matlab实现R-CNN目标检测

clear;clc;close all %% 步骤1： 载入AlexNet % net=alexnet; net =load('alexnet.mat'); net =net.netTransfer; %% 步骤2：训练R-CNN检测器 % 载入训练集 data = load('stopSignsAndCars.mat', 'stopSignsAndCars'); stopSignsAndCars = data.stopSignsAndCars; % 设置图像路径参数 visiondata = fullfile(toolboxdir('vision'),'visiondata'); stopSignsAndCars.imageFilename = fullfile(visiondata, stopSignsAndCars.imageFilename); % 显示数据 summary(stopSignsAndCars) % 只保留文件名及其所包含的“stop sign”区域 stopSigns = stopSignsAndCars(:, {'imageFilename','stopSign'}); % 显示一张照片及其所包含的真实“stop sign”区域 I = imread(stopSigns.imageFilename{1}); I = insertObjectAnnotation(I,'Rectangle',stopSigns.stopSign{1},'stop sign','LineWidth',8); figure imshow(I) % 设置训练策略 options = trainingOptions('sgdm', ... 'MiniBatchSize', 128, ... 'InitialLearnRate', 1e-3, ... 'LearnRateSchedule', 'piecewise', ... 'LearnRateDropFactor', 0.1, ... 'LearnRateDropPeriod', 100, ... 'MaxEpochs', 2, ... 'Plots','training-progress', ... 'Verbose', true); % 训练网络. rcnn = trainRCNNObjectDetector(stopSigns, net, options, ... 'NegativeOverlapRange', [0 0.3], 'PositiveOverlapRange',[0.5 1]) %% 步骤5：检验检测器的对“Stop sign”交通标志的图像的检测效果 % 载入测试图片 testImage = imread('stopSignTest.jpg'); % 检测“stop sign“标志 [bboxes,score,label] = detect(rcnn,testImage,'MiniBatchSize',128) % 计算置信度并显示 [score, idx] = max(score); bbox = bboxes(idx, :); annotation = sprintf('%s: (Confidence = %f)', label(idx), score); outputImage = insertObjectAnnotation(testImage, 'rectangle', bbox, annotation); figure imshow(outputImage)