Create Faster R-CNN Object Detection Network

This example builds upon the Create Fast R-CNN Object Detection Network example above. It transforms a pretrained ResNet-50 network into a Faster R-CNN object detection network by adding an ROI pooling layer, a bounding box regression layer, and a region proposal network (RPN). The Faster R-CNN network can then be trained using trainFasterRCNNObjectDetector.

Create Fast R-CNN Network

Start by creating Fast R-CNN, which forms the basis of Faster R-CNN. The Create Fast R-CNN Object Detection Network example explains this section of code in detail.

% Load a pretrained ResNet-50.
net = resnet50;
lgraph = layerGraph(net);

% Remove the last 3 layers. 
layersToRemove = {
    'fc1000'
    'fc1000_softmax'
    'ClassificationLayer_fc1000'
    };
lgraph = removeLayers(lgraph, layersToRemove);

% Specify the number of classes the network should classify.
numClasses = 2;
numClassesPlusBackground = numClasses + 1;

% Define new classification layers.
newLayers = [
    fullyConnectedLayer(numClassesPlusBackground, 'Name', 'rcnnFC')
    softmaxLayer('Name', 'rcnnSoftmax')
    classificationLayer('Name', 'rcnnClassification')
    ];

% Add new object classification layers.
lgraph = addLayers(lgraph, newLayers);

% Connect the new layers to the network. 
lgraph = connectLayers(lgraph, 'avg_pool', 'rcnnFC');

% Define the number of outputs of the fully connected layer.
numOutputs = 4 * numClasses;

% Create the box regression layers.
boxRegressionLayers = [
    fullyConnectedLayer(numOutputs,'Name','rcnnBoxFC')
    rcnnBoxRegressionLayer('Name','rcnnBoxDeltas')
    ];

% Add the layers to the network.
lgraph = addLayers(lgraph, boxRegressionLayers);

% Connect the regression layers to the layer named 'avg_pool'.
lgraph = connectLayers(lgraph,'avg_pool','rcnnBoxFC');

% Select a feature extraction layer.
featureExtractionLayer = 'activation_40_relu';

% Disconnect the layers attached to the selected feature extraction layer.
lgraph = disconnectLayers(lgraph, featureExtractionLayer,'res5a_branch2a');
lgraph = disconnectLayers(lgraph, featureExtractionLayer,'res5a_branch1');

% Add ROI max pooling layer.
outputSize = [14 14];
roiPool = roiMaxPooling2dLayer(outputSize,'Name','roiPool');
lgraph = addLayers(lgraph, roiPool);

% Connect feature extraction layer to ROI max pooling layer.
lgraph = connectLayers(lgraph, featureExtractionLayer,'roiPool/in');

% Connect the output of ROI max pool to the disconnected layers from above.
lgraph = connectLayers(lgraph, 'roiPool','res5a_branch2a');
lgraph = connectLayers(lgraph, 'roiPool','res5a_branch1');

Add Region Proposal Network (RPN)

Faster R-CNN uses a region proposal network (RPN) to generate region proposals. An RPN produces region proposals by predicting the class, “object” or “background”, and box offsets for a set of predefined bounding box templates known as "anchor boxes". Anchor boxes are specified by providing their size, which is typically determined based on a priori knowledge of the scale and aspect ratio of objects in the training dataset.

Learn more about Anchor Boxes for Object Detection.

Define the anchor boxes and create a regionProposalLayer.

% Define anchor boxes.
anchorBoxes = [
    16 16
    32 16
    16 32
    ];

% Create the region proposal layer.
proposalLayer = regionProposalLayer(anchorBoxes,'Name','regionProposal');

lgraph = addLayers(lgraph, proposalLayer);

Add the convolution layers for RPN and connect it to the feature extraction layer selected above.

% Number of anchor boxes.
numAnchors = size(anchorBoxes,1);

% Number of feature maps in coming out of the feature extraction layer. 
numFilters = 1024;

rpnLayers = [
    convolution2dLayer(3, numFilters,'padding',[1 1],'Name','rpnConv3x3')
    reluLayer('Name','rpnRelu')
    ];

lgraph = addLayers(lgraph, rpnLayers);

% Connect to RPN to feature extraction layer.
lgraph = connectLayers(lgraph, featureExtractionLayer, 'rpnConv3x3');

Add the RPN classification output layers. The classification layer classifies each anchor as "object" or "background".

% Add RPN classification layers.
rpnClsLayers = [
    convolution2dLayer(1, numAnchors*2,'Name', 'rpnConv1x1ClsScores')
    rpnSoftmaxLayer('Name', 'rpnSoftmax')
    rpnClassificationLayer('Name','rpnClassification')
    ];
lgraph = addLayers(lgraph, rpnClsLayers);

% Connect the classification layers to the RPN network.
lgraph = connectLayers(lgraph, 'rpnRelu', 'rpnConv1x1ClsScores');

Add the RPN regression output layers. The regression layer predicts 4 box offsets for each anchor box.

% Add RPN regression layers.
rpnRegLayers = [
    convolution2dLayer(1, numAnchors*4, 'Name', 'rpnConv1x1BoxDeltas')
    rcnnBoxRegressionLayer('Name', 'rpnBoxDeltas');
    ];

lgraph = addLayers(lgraph, rpnRegLayers);

% Connect the regression layers to the RPN network.
lgraph = connectLayers(lgraph, 'rpnRelu', 'rpnConv1x1BoxDeltas');

Finally, connect the classification and regression feature maps to the region proposal layer inputs, and the ROI pooling layer to the region proposal layer output.

% Connect region proposal network.
lgraph = connectLayers(lgraph, 'rpnConv1x1ClsScores', 'regionProposal/scores');
lgraph = connectLayers(lgraph, 'rpnConv1x1BoxDeltas', 'regionProposal/boxDeltas');

% Connect region proposal layer to roi pooling.
lgraph = connectLayers(lgraph, 'regionProposal', 'roiPool/roi');

% Show the network after adding the RPN layers.
figure
plot(lgraph)
ylim([30 42])

The network is ready to be trained using trainFasterRCNNObjectDetector.