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yolov3ObjectDetector

Detect objects using YOLO v3 object detector

Since R2021a

Description

The yolov3ObjectDetector object creates a you only look once version 3 (YOLO v3) object detector for detecting objects in an image. Using this object, you can:

  • Create a pretrained YOLO v3 object detector by using YOLO v3 deep learning networks trained on COCO dataset.

  • Create a custom YOLO v3 object detector by using any pretrained or untrained YOLO v3 deep learning network.

Creation

Description

Pretrained YOLO v3 Object Detector

example

detector = yolov3ObjectDetector(name) creates a pretrained YOLO v3 object detector by using YOLO v3 deep learning networks trained on a COCO dataset.

Note

To use the pretrained YOLO v3 deep learning networks trained on COCO dataset, you must install the Computer Vision Toolbox™ Model for YOLO v3 Object Detection from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons. To run this function, you will require the Deep Learning Toolbox™.

Custom YOLO v3 Object Detector

detector = yolov3ObjectDetector(name,classes,aboxes) creates a pretrained YOLO v3 object detector and configures it to perform transfer learning using a specified set of object classes and anchor boxes. For optimal results, you must train the detector on new training images before performing detection.

detector = yolov3ObjectDetector(net,classes,aboxes) creates an object detector by using the deep learning network net.

If net is a pretrained YOLO v3 deep learning network, the function creates a pretrained YOLO v3 object detector. The classes and aboxes are values used for training the network.

If net is an untrained YOLO v3 deep learning network, the function creates a YOLO v3 object detector to use for training and inference. classes and aboxes specify the object classes and the anchor boxes, respectively, for training the YOLO v3 network.

You must train the detector on a training dataset before performing object detection. For information about how to train a YOLO v3 object detector, see Preprocess Training Data and Train Model sections in the Object Detection Using YOLO v3 Deep Learning example.

example

detector = yolov3ObjectDetector(baseNet,classes,aboxes,'DetectionNetworkSource',layer) creates a YOLO v3 object detector by adding detection heads to a base network, baseNet.

The function adds detection heads to the specified feature extraction layers layer in the base network. To specify the names of the feature extraction layers, use the name-value argument 'DetectionNetworkSource',layer.

If baseNet is a pretrained deep learning network, the function creates a YOLO v3 object detector and configures it to perform transfer learning with the specified object classes and anchor boxes.

If baseNet is an untrained deep learning network, the function creates a YOLO v3 object detector and configures it for object detection. classes and aboxes specify the object classes and the anchor boxes, respectively, for training the YOLO v3 network.

You must train the detector on a training dataset before performing object detection.

detector = yolov3ObjectDetector(___,Name=Value) sets the InputSize and ModelName properties of the object detector by using name-value arguments in addition to any combination of input arguments from previous syntaxes. For example, InputSize=[224 224 3] sets the size of the images used for training to [224 224 3].

Input Arguments

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Name of the pretrained YOLO v3 deep learning network, specified as one of these:

  • 'darknet53-coco' — A pretrained YOLO v3 deep learning network created using DarkNet-53 as the base network and trained on COCO dataset.

  • 'tiny-yolov3-coco' — A pretrained YOLO v3 deep learning network created using a small base network and trained on COCO dataset.

Data Types: char | string

Names of object classes for training the detector, specified as a string vector, cell array of character vectors, or categorical vector. This argument sets the ClassNames property of the yolov3ObjectDetector object.

Data Types: char | string | categorical

Anchor boxes for training the detector, specified as an N-by-1 cell array. N is the number of output layers in the YOLO v3 deep learning network. Each cell contains an M-by-2 matrix, where M is the number of anchor boxes in that layer. Each cell can contain a different number of anchor boxes. Each row in the M-by-2 matrix denotes the size of an anchor box in the form [height width].

The first element in the cell array specifies the anchor boxes to associate with the first output layer, the second element in the cell array specifies the anchor boxes to associate with the second output layer, and so on. For accurate detection results, specify large anchor boxes for the first output layer and small anchor boxes for the last output layer. That is, the anchor box sizes must decrease for each output layer in the order in which the layers appear in the YOLO v3 deep learning network.

This argument sets the AnchorBoxes property of the yolov3ObjectDetector object.

Data Types: cell

YOLO v3 deep learning network, specified as a dlnetwork (Deep Learning Toolbox) object. The input network can be either an untrained or a pretrained deep learning network.

Base network for creating the YOLO v3 deep learning network, specified as a dlnetwork (Deep Learning Toolbox) object. The network can be either an untrained or a pretrained deep learning network.

Names of the feature extraction layers in the base network, specified as a cell array of character vectors, or a string array. The function creates a YOLO v3 network by adding detection head layers to the output of the feature extraction layers in the base network.

Example: layer = {'conv10','fire9-concat','fire8-concat'}

Example: layer = ["conv10","fire9-concat","fire8-concat"]

Data Types: char | string | cell

Properties

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This property is read-only.

YOLO v3 deep learning network to use for object detection, stored as a dlnetwork (Deep Learning Toolbox) object.

This property is read-only.

Set of anchor boxes, stored as a N-by-1 cell array. N is the number of output layers in the YOLO v3 deep learning network for which the anchor boxes are defined. Each element in the cell is a M-by-2 matrix. M denotes the number of anchor boxes. Each cell can contain a different number of anchor boxes. Each row in the M-by-2 matrix denotes the size of the anchor box in the form of [height width]. The first element in the cell array specifies the anchor boxes for the first output layer, the second element in the cell array specifies the anchor boxes for the second output layer, and so on.

You can set this property by using the input argument aboxes.

This property is read-only.

Names of object classes to detect, stored as a categorical vector. You can set this property by using the input argument classes.

This property is read-only.

Image size used for training, stored as a vector of form [height width] or [height width channels]. To set this property, specify it at object creation.

For example, detector = yolov3ObjectDetector(net,classes,aboxes,InputSize=[224 224 3]).

Name for the object detector, stored as a character vector. To set this property, specify it at object creation.

For example, yolov3ObjectDetector(net,classes,aboxes,'ModelName','customDetector') sets the name for the object detector to 'customDetector'.

This property is read-only.

Bounding box format for an object detector, stored as "axis-aligned" or "rotated". When the PredictedBoxType is "axis-aligned", the object detector will train and perform inference on only axis-aligned bounding boxes. If it is set to "rotated", the object detector will train and perform inference on only rotated bounding boxes. Set this property when you create the object.

Object Functions

detectDetect objects using YOLO v3 object detector
preprocessPreprocess training and test images
forwardCompute YOLO v3 deep learning network output for training
predictCompute YOLO v3 deep learning network outputs for inference

Examples

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Specify the name of a pretrained YOLO v3 deep learning network.

name = 'tiny-yolov3-coco';

Create YOLO v3 object detector by using the pretrained YOLO v3 network.

detector = yolov3ObjectDetector(name);

Display and inspect the properties of the YOLO v3 object detector.

disp(detector)
  yolov3ObjectDetector with properties:

             Network: [1x1 dlnetwork]
         AnchorBoxes: {2x1 cell}
          ClassNames: [80x1 categorical]
           InputSize: [416 416 3]
    PredictedBoxType: 'axis-aligned'
           ModelName: 'tiny-yolov3-coco'

Use analyzeNetwork to display the YOLO v3 network architecture and get information about the network layers. The network has two detection heads attached to the feature extraction network.

analyzeNetwork(detector.Network)

Detect objects in an unknown image by using the pretrained YOLO v3 object detector.

img = imread('sherlock.jpg');
img = preprocess(detector,img);
img = im2single(img);
[bboxes,scores,labels] = detect(detector,img,'DetectionPreprocessing','none');

Display the detection results.

detectedImg = insertObjectAnnotation(img,'Rectangle',bboxes,labels);
figure
imshow(detectedImg)

This example shows how to create a custom YOLO v3 object detector by using a pretrained SqueezeNet as the base network.

Load a pretrained SqueezeNet network. The SqueezeNet network is a convolutional neural network that you can use as the base network for creating a YOLO v3 object detector.

net = imagePretrainedNetwork("squeezenet");

Inspect the architecture of the base network by using analyzeNetwork (Deep Learning Toolbox) function.

analyzeNetwork(net)

Specify the anchor boxes and the classes to use to train the YOLO v3 network.

aboxes = {[150,127;97,90;68,67];[38,42;41,29;31,23]};
classes = ["Car","Truck"];

Select two feature extraction layers in the base network to serve as the source for detection subnetwork.

layer = ["fire9-concat","fire8-concat"];

Create a custom YOLO v3 object detector by adding detection heads to the feature extraction layers of the base network. Specify the model name, classes, and the anchor boxes.

detector = yolov3ObjectDetector(net,classes,aboxes, ...
    ModelName="Custom YOLO v3",DetectionNetworkSource=layer);

Inspect the architecture of the YOLO v3 deep learning network by using analyzeNetwork (Deep Learning Toolbox) function.

analyzeNetwork(detector.Network)

Inspect the properties of the YOLO v3 object detector. You can now train the YOLO v3 object detector on a custom training dataset and perform object detection.

disp(detector)
  yolov3ObjectDetector with properties:

             Network: [1x1 dlnetwork]
         AnchorBoxes: {2x1 cell}
          ClassNames: [2x1 categorical]
           InputSize: [227 227 3]
    PredictedBoxType: 'axis-aligned'
           ModelName: 'Custom YOLO v3'

For information about how to train a YOLO v3 object detector, see the Object Detection Using YOLO v3 Deep Learning example.

Extended Capabilities

Version History

Introduced in R2021a

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