matlab-基于yolo2的车辆检测识别matlab仿真-源码

clc; clear; close all; warning off; addpath(genpath(pwd)); rng('default'); %**************************************************************************** %更多关于matlab和fpga的搜索“fpga和matlab”的CSDN博客: %matlab/FPGA项目开发合作 %https://blog.csdn.net/ccsss22?type=blog %**************************************************************************** doTraining = false; if ~doTraining && ~exist('yolov2ResNet50VehicleExample_19b.mat','file') disp('Downloading pretrained detector (98 MB)...'); pretrainedURL = 'https://www.mathworks.com/supportfiles/vision/data/yolov2ResNet50VehicleExample_19b.mat'; websave('yolov2ResNet50VehicleExample_19b.mat',pretrainedURL); end unzip vehicleDatasetImages.zip data = load('vehicleDatasetGroundTruth.mat'); vehicleDataset = data.vehicleDataset; vehicleDataset(1:4,:) vehicleDataset.imageFilename = fullfile(pwd,vehicleDataset.imageFilename); rng(randi(1000)); shuffledIndices = randperm(height(vehicleDataset)); idx = floor(0.6 * length(shuffledIndices) ); trainingIdx = 1:idx; trainingDataTbl = vehicleDataset(shuffledIndices(trainingIdx),:); validationIdx = idx+1 : idx + 1 + floor(0.1 * length(shuffledIndices) ); validationDataTbl = vehicleDataset(shuffledIndices(validationIdx),:); testIdx = validationIdx(end)+1 : length(shuffledIndices); testDataTbl = vehicleDataset(shuffledIndices(testIdx),:); imdsTrain = imageDatastore(trainingDataTbl{:,'imageFilename'}); bldsTrain = boxLabelDatastore(trainingDataTbl(:,'vehicle')); imdsValidation = imageDatastore(validationDataTbl{:,'imageFilename'}); bldsValidation = boxLabelDatastore(validationDataTbl(:,'vehicle')); imdsTest = imageDatastore(testDataTbl{:,'imageFilename'}); bldsTest = boxLabelDatastore(testDataTbl(:,'vehicle')); trainingData = combine(imdsTrain,bldsTrain); validationData = combine(imdsValidation,bldsValidation); testData = combine(imdsTest,bldsTest); data = read(trainingData); I = data{1}; bbox = data{2}; annotatedImage = insertShape(I,'Rectangle',bbox); annotatedImage = imresize(annotatedImage,2); figure imshow(annotatedImage) inputSize = [224 224 3]; numClasses = width(vehicleDataset)-1; trainingDataForEstimation = transform(trainingData,@(data)preprocessData(data,inputSize)); numAnchors = 7; [anchorBoxes, meanIoU] = estimateAnchorBoxes(trainingDataForEstimation, numAnchors); featureExtractionNetwork = resnet50; featureLayer = 'activation_40_relu'; lgraph = yolov2Layers(inputSize,numClasses,anchorBoxes,featureExtractionNetwork,featureLayer); augmentedTrainingData = transform(trainingData,@augmentData); augmentedData = cell(4,1); for k = 1:4 data = read(augmentedTrainingData); augmentedData{k} = insertShape(data{1},'Rectangle',data{2}); reset(augmentedTrainingData); end figure montage(augmentedData,'BorderSize',10) preprocessedTrainingData = transform(augmentedTrainingData,@(data)preprocessData(data,inputSize)); preprocessedValidationData = transform(validationData,@(data)preprocessData(data,inputSize)); data = read(preprocessedTrainingData); I = data{1}; bbox = data{2}; annotatedImage = insertShape(I,'Rectangle',bbox); annotatedImage = imresize(annotatedImage,2); figure imshow(annotatedImage) analyzeNetwork(lgraph); options = trainingOptions('sgdm', ... 'MiniBatchSize',16, .... 'InitialLearnRate',1e-3, ... 'MaxEpochs',16,... 'Shuffle','every-epoch', ... 'CheckpointPath',tempdir, ... 'ValidationData',preprocessedValidationData,... 'ValidationFrequency',40,... 'Verbose',false,... 'Plots','training-progress'); if doTraining [detector,info] = trainYOLOv2ObjectDetector(preprocessedTrainingData,lgraph,options); else pretrained = load('yolov2ResNet50VehicleExample_19b.mat'); detector = pretrained.detector; end