1.完整项目描述和程序获取
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2.部分仿真图预览
3.算法概述
Faster-RCNN是一种流行的深度学习目标检测算法，它通过使用Region Proposal Network (RPN) 来实现高效且准确的目标检测。相比于其它的目标检测算法，例如R-CNN和SPP-Net，Faster-RCNN具有更高的效率和准确性。
4.部分源码
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% 随机打乱数据集并分割为训练集、验证集和测试集
Ridx = randperm(height(vehicleDataset));
idx = floor(0.85 * height(vehicleDataset));
train_Idx = 1:idx;
train_Tbl = vehicleDataset(Ridx(train_Idx),:);
test_Idx = idx+1 : idx + 1 + floor(0.1 * length(Ridx) );
test_Tbl = vehicleDataset(Ridx(test_Idx),:);
test_Idx0 = test_Idx(end)+1 : length(Ridx);
test_Tbl0 = vehicleDataset(Ridx(test_Idx0),:);
% 创建图像数据存储器
imdsTrain = imageDatastore(train_Tbl{:,'imageFilename'});
bldsTrain = boxLabelDatastore(train_Tbl(:,'man'));
imdsValidation = imageDatastore(test_Tbl{:,'imageFilename'});
bldsValidation = boxLabelDatastore(test_Tbl(:,'man'));
imdsTest = imageDatastore(test_Tbl0{:,'imageFilename'});
bldsTest = boxLabelDatastore(test_Tbl0(:,'man'));
% 创建训练、验证和测试数据
trainingData = combine(imdsTrain,bldsTrain);
validationData = combine(imdsValidation,bldsValidation);
testData = combine(imdsTest,bldsTest);
% 预处理训练数据
data = read(trainingData);
In_layer_Size = [224 224 3];
% 估计锚框
pre_train_data = transform(trainingData, @(data)preprocessData(data,In_layer_Size));
NAnchor = 3;
NBoxes = estimateAnchorBoxes(pre_train_data,NAnchor);
numClasses = width(vehicleDataset)-1;
% 创建Faster R-CNN网络
lgraph = fasterRCNNLayers(In_layer_Size,numClasses,NBoxes,Initial_nn,featureLayer);
% 数据增强
aug_train_data = transform(trainingData,@augmentData);
augmentedData = cell(4,1);
% 预处理数据并显示标注
trainingData = transform(aug_train_data,@(data)preprocessData(data,In_layer_Size));
validationData = transform(validationData,@(data)preprocessData(data,In_layer_Size));
data = read(trainingData);
I = data{1};
bbox = data{2};
% 设置训练参数
options = trainingOptions('sgdm',...
'MaxEpochs',240,...
'MiniBatchSize',2,...
'InitialLearnRate',3e-5,...
'CheckpointPath',tempdir,...
'ValidationData',validationData);
% 训练Faster R-CNN目标检测器
[detector, info] = trainFasterRCNNObjectDetector(trainingData,lgraph,options,'NegativeOverlapRange',[0 0.15],'PositiveOverlapRange',[0.15 1]);
save net015.mat detector info
0Y_012m
