使用 YOLO v2 深度学习进行目标检测

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;

% Display first few rows of the data set.
vehicleDataset(1:4,:)

% Add the fullpath to the local vehicle data folder.
vehicleDataset.imageFilename = fullfile(pwd,vehicleDataset.imageFilename);

rng(0);
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);

% Visualize the augmented images.
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)

options = trainingOptions('sgdm', ...
        'MiniBatchSize',16, ....
        'InitialLearnRate',1e-3, ...
        'MaxEpochs',20, ... 
        'CheckpointPath',tempdir, ...
        'ValidationData',preprocessedValidationData);

if doTraining       
    % Train the YOLO v2 detector.
    [detector,info] = trainYOLOv2ObjectDetector(preprocessedTrainingData,lgraph,options);
else
    % Load pretrained detector for the example.
    pretrained = load('yolov2ResNet50VehicleExample_19b.mat');
    detector = pretrained.detector;
end

I = imread('highway.png');
I = imresize(I,inputSize(1:2));
[bboxes,scores] = detect(detector,I);

I = insertObjectAnnotation(I,'rectangle',bboxes,scores);
figure
imshow(I)

preprocessedTestData = transform(testData,@(data)preprocessData(data,inputSize));

detectionResults = detect(detector, preprocessedTestData);

metrics = evaluateObjectDetection(detectionResults,preprocessedTestData);
classID = 1;
precision = metrics.ClassMetrics.Precision{classID};
recall = metrics.ClassMetrics.Recall{classID};

figure
plot(recall,precision)
xlabel('Recall')
ylabel('Precision')
grid on
title(sprintf('Average Precision = %.2f',metrics.ClassMetrics.mAP(classID)))

function B = augmentData(A)
% Apply random horizontal flipping, and random X/Y scaling. Boxes that get
% scaled outside the bounds are clipped if the overlap is above 0.25. Also,
% jitter image color.

B = cell(size(A));

I = A{1};
sz = size(I);
if numel(sz)==3 && sz(3) == 3
    I = jitterColorHSV(I,...
        'Contrast',0.2,...
        'Hue',0,...
        'Saturation',0.1,...
        'Brightness',0.2);
end

% Randomly flip and scale image.
tform = randomAffine2d('XReflection',true,'Scale',[1 1.1]);
rout = affineOutputView(sz,tform,'BoundsStyle','CenterOutput');
B{1} = imwarp(I,tform,'OutputView',rout);

% Sanitize boxes, if needed. This helper function is attached as a
% supporting file. Open the example in MATLAB to access this function.
A{2} = helperSanitizeBoxes(A{2});

% Apply same transform to boxes.
[B{2},indices] = bboxwarp(A{2},tform,rout,'OverlapThreshold',0.25);
B{3} = A{3}(indices);

% Return original data only when all boxes are removed by warping.
if isempty(indices)
    B = A;
end
end

function data = preprocessData(data,targetSize)
% Resize image and bounding boxes to the targetSize.
sz = size(data{1},[1 2]);
scale = targetSize(1:2)./sz;
data{1} = imresize(data{1},targetSize(1:2));

% Sanitize boxes, if needed. This helper function is attached as a
% supporting file. Open the example in MATLAB to access this function.
data{2} = helperSanitizeBoxes(data{2});

% Resize boxes to new image size.
data{2} = bboxresize(data{2},scale);
end