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rcnnObjectDetector class

Detect objects using R-CNN deep learning detector

Syntax

rcnnObjectDetector

Description

example

rcnnObjectDetector contains a trained R-CNN (regions with convolutional neural networks) object detector returned by the trainRCNNObjectDetector function.

You must have a Statistics and Machine Learning Toolbox™ license to use this classifier.

Use of this function requires Neural Network Toolbox™, Statistics and Machine Learning Toolbox, and a CUDA®-enabled NVIDIA® GPU with a compute capability of 3.0 or higher.

Properties

expand all

Series network object representing the convolutional neural network (CNN), specified as an SeriesNetwork object. The object is used within the R-CNN detector.

Region proposal method, specified as a function handle.

Object class names, specified as a cell array. The array contains the names of the object classes the R-CNN detector was trained to find.

Methods

classifyRegionsClassifies objects within regions
detectDetect objects using R-CNN deep learning detector

Examples

expand all

Load training data and network layers.

load('rcnnStopSigns.mat', 'stopSigns', 'layers')

Add the image directory to the MATLAB path.

imDir = fullfile(matlabroot, 'toolbox', 'vision', 'visiondata',...
  'stopSignImages');
addpath(imDir);

Set network training options to use mini-batch size of 32 to reduce GPU memory usage. Lower the InitialLearningRate to reduce the rate at which network parameters are changed. This is beneficial when fine-tuning a pre-trained network and prevents the network from changing too rapidly.

options = trainingOptions('sgdm', ...
  'MiniBatchSize', 32, ...
  'InitialLearnRate', 1e-6, ...
  'MaxEpochs', 10);

Train the R-CNN detector. Training can take a few minutes to complete.

rcnn = trainRCNNObjectDetector(stopSigns, layers, options, 'NegativeOverlapRange', [0 0.3]);
*******************************************************************
Training an R-CNN Object Detector for the following object classes:

* stopSign

Step 1 of 3: Extracting region proposals from 27 training images...done.

Step 2 of 3: Training a neural network to classify objects in training data...

|=========================================================================================|
|     Epoch    |   Iteration  | Time Elapsed |  Mini-batch  |  Mini-batch  | Base Learning|
|              |              |  (seconds)   |     Loss     |   Accuracy   |     Rate     |
|=========================================================================================|
|            3 |           50 |         9.27 |       0.2895 |       96.88% |     0.000001 |
|            5 |          100 |        14.77 |       0.2443 |       93.75% |     0.000001 |
|            8 |          150 |        20.29 |       0.0013 |      100.00% |     0.000001 |
|           10 |          200 |        25.94 |       0.1524 |       96.88% |     0.000001 |
|=========================================================================================|

Network training complete.

Step 3 of 3: Training bounding box regression models for each object class...100.00%...done.

R-CNN training complete.
*******************************************************************

Test the R-CNN detector on a test image.

img = imread('stopSignTest.jpg');

[bbox, score, label] = detect(rcnn, img, 'MiniBatchSize', 32);

Display strongest detection result.

[score, idx] = max(score);

bbox = bbox(idx, :);
annotation = sprintf('%s: (Confidence = %f)', label(idx), score);

detectedImg = insertObjectAnnotation(img, 'rectangle', bbox, annotation);

figure
imshow(detectedImg)

Remove the image directory from the path.

rmpath(imDir);

Resume training an R-CNN object detector using additional data. To illustrate this procedure, half the ground truth data will be used to initially train the detector. Then, training is resumed using all the data.

Load training data and initialize training options.

load('rcnnStopSigns.mat', 'stopSigns', 'layers')

stopSigns.imageFilename = fullfile(toolboxdir('vision'),'visiondata', ...
    stopSigns.imageFilename);

options = trainingOptions('sgdm', ...
    'MiniBatchSize', 32, ...
    'InitialLearnRate', 1e-6, ...
    'MaxEpochs', 10, ...
    'Verbose', false);

Train the R-CNN detector with a portion of the ground truth.

rcnn = trainRCNNObjectDetector(stopSigns(1:10,:), layers, options, 'NegativeOverlapRange', [0 0.3]);

Get the trained network layers from the detector. When you pass in an array of network layers to trainRCNNObjectDetector, they are used as-is to continue training.

network = rcnn.Network;
layers = network.Layers;

Resume training using all the training data.

rcnnFinal = trainRCNNObjectDetector(stopSigns, layers, options);

Create an R-CNN object detector for two object classes: dogs and cats.

objectClasses = {'dogs','cats'};

The network must be able to classify both dogs, cats, and a "background" class in order to be trained using trainRCNNObjectDetector. In this example, a one is added to include the background.

numClassesPlusBackground = numel(objectClasses) + 1;

The final fully connected layer of a network defines the number of classes that the network can classify. Set the final fully connected layer to have an output size equal to the number of classes plus a background class.

layers = [ ...
    imageInputLayer([28 28 1])
    convolution2dLayer(5,20)        
    fullyConnectedLayer(numClassesPlusBackground);
    softmaxLayer()
    classificationLayer()];

These network layers can now be used to train an R-CNN two-class object detector.

Create an R-CNN object detector and set it up to use a saved network checkpoint. A network checkpoint is saved every epoch during network training when the trainingOptions 'CheckpointPath' parameter is set. Network checkpoints are useful in case your training session terminates unexpectedly.

Load the stop sign training data.

load('rcnnStopSigns.mat','stopSigns','layers')

Add full path to image files.

stopSigns.imageFilename = fullfile(toolboxdir('vision'),'visiondata', ...
      stopSigns.imageFilename);

Set the 'CheckpointPath' using the trainingOptions function.

checkpointLocation = tempdir;
options = trainingOptions('sgdm','Verbose',false, ...
    'CheckpointPath',checkpointLocation);

Train the R-CNN object detector with a few images.

rcnn = trainRCNNObjectDetector(stopSigns(1:3,:),layers,options);

Load a saved network checkpoint.

wildcardFilePath = fullfile(checkpointLocation,'convnet_checkpoint__*.mat');
contents = dir(wildcardFilePath);

Load one of the checkpoint networks.

filepath = fullfile(contents(1).folder,contents(1).name);
checkpoint = load(filepath);

checkpoint.net
ans = 

  SeriesNetwork with properties:

    Layers: [15×1 nnet.cnn.layer.Layer]

Create a new R-CNN object detector and set it up to use the saved network.

rcnnCheckPoint = rcnnObjectDetector();
rcnnCheckPoint.RegionProposalFcn = @rcnnObjectDetector.proposeRegions;

Set the Network to the saved network checkpoint.

rcnnCheckPoint.Network = checkpoint.net
rcnnCheckPoint = 

  rcnnObjectDetector with properties:

              Network: [1×1 SeriesNetwork]
           ClassNames: {'stopSign'  'Background'}
    RegionProposalFcn: @rcnnObjectDetector.proposeRegions

Introduced in R2016b