Deep Learning Object Detector - Detect objects using trained deep learning object detector - Simulink (original) (raw)

Detect objects using trained deep learning object detector

Since R2021b

Description

The Deep Learning Object Detector block predicts bounding boxes, class labels, and scores for the input image by using the trained object detector specified through the block parameter. This block allows loading of a pretrained object detector into the Simulink® model from a MAT file or from a MATLAB® function. This block provides a graphical interface for using the detector objects in Simulink. To enable some parameters of the Deep Learning Object Detector block you must choose an object detector that supports those parameters. For example, using ayolov2ObjectDetector object with this block enables you to select parameters similar to the name-value arguments of the associated detect object function.

Examples

Ports

Input

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Image — Image data

array

An _H_-by-_W_-by-C numeric array, where H, W, and C are the height, width, and number of channels of the image, respectively. Only one image per time step is allowed as input.

Output

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Bboxes — Locations of objects detected

matrix

Locations of the objects detected within the input image, returned as an_M_-by-4 or an _M_-by-5 matrix.M is the number of bounding boxes detected in the image. You can put an upper bound to the size M by specifying theMaximum Number of Detections parameter.

The table describes the format of bounding boxes.

Bounding Box	Description
Axis-aligned rectangle	Defined in spatial coordinates as an _M_-by-4 numeric matrix with rows of the form [x y w _h_], where: M is the number of axis-aligned rectangles.x and y specify the upper-left corner of the rectangle.w specifies the width of the rectangle, which is its length along the _x_-axis.h specifies the height of the rectangle, which is its length along the _y_-axis.
Rotated rectangle	Defined in spatial coordinates as an _M_-by-5 numeric matrix with rows of the form [xctr yctr w h _yaw_], where: M is the number of rotated rectangles.xctr and yctr specify the center of the rectangle.w specifies the width of the rectangle, which is its length along the _x_-axis before rotation.h specifies the height of the rectangle, which is its length along the _y_-axis before rotation.yaw specifies the rotation angle in degrees. The rotation is clockwise-positive around the center of the bounding box.

For more information, see Datastores for Deep Learning (Deep Learning Toolbox).

Labels — Labels for bounding boxes

vector

Labels for the bounding boxes, returned as an _M_-by-1 enumerated vector. M is the number of bounding boxes detected in the image.

Scores — Detection scores

vector

Detection confidence scores for each label, returned as an_M_-by-1 vector. M is the number of bounding boxes detected in the image. A higher score indicates higher confidence in the detection.

Parameters

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Detector — Source for trained detector object

Detector from MAT file (default) | Detector from MATLAB function

Select the source for the detector object from these options:

• Detector from MAT file — Import a detector object from a MAT file. For example, select a MAT file containing ayolov2ObjectDetector object.
• Detector from MATLAB function — Import a detector object from a MATLAB function. For example, specify the functionvehicleDetectorYOLOv2, which returns a trainedyolov2ObjectDetector object.

The imported detector must be one of these supported objects:

• rcnnObjectDetector
• fastRCNNObjectDetector
• fasterRCNNObjectDetector
• rtmdetObjectDetector
• ssdObjectDetector
• yolov2ObjectDetector
• yolov3ObjectDetector
• yolov4ObjectDetector
• yoloxObjectDetector

Programmatic Use

File path — MAT file containing detector object

untitled.mat (default) | MAT file name

This parameter specifies the name of the MAT file that contains the detector object to load. If the file is not on the MATLAB path, use the Browse button to locate the file.

Dependencies

To enable this parameter, set the Detector parameter toDetector from MAT file.

Programmatic Use

MATLAB function — MATLAB function name

untitled (default) | MATLAB function name

This parameter specifies the name of the MATLAB function that returns a trained object detector. For example, specify the function vehicleDetectorYOLOv2, which returns a trainedyolov2ObjectDetector object, or specify a custom function.

Dependencies

To enable this parameter, set the Detector parameter toDetector from MATLAB function.

Programmatic Use

Region of interest — Search region of interest

vector of the form [x y width _height_]

Specify the search region of interest as vector of the form [_x y width height_]. The vector specifies the upper-left corner and size of a region in pixels.

Dependencies

To enable this parameter, select the Specify region of interest parameter.

Programmatic Use

Detection threshold — Detection threshold

scalar in the range [0, 1]

Specify the detection threshold as scalar in the range [0, 1]. Detections that have scores lower than this threshold value are removed. To reduce false positives, increase this value.

Dependencies

To enable this parameter, you must use a detector that supports theDetection threshold parameter. For example, use ayolov2ObjectDetector object.

Programmatic Use

Number of Strongest Regions — Maximum number of strongest region proposals

2000 (default) | positive integer

Specify the maximum number of strongest region proposals as an integer. Reduce this value to speed up processing at the cost of detection accuracy. To use all region proposals, specify this parameter as Inf.

Dependencies

To enable this parameter, use a detector that supports the Number of Strongest Regions parameter. For example, use anrcnnObjectDetector object.

Programmatic Use

Maximum Region Size — Maximum region size

vector of the form [height _width_]

Specify the maximum region size as a vector of the form [height _width_]. Units are in pixels. The maximum region size defines the size of the largest region containing the object. For example, [50 50] sets the size of the largest region containing the object to50-by-50 pixels. To reduce computation time, set this value to the known maximum region size for the objects that can be detected in the input test images.

Dependencies

To enable this parameter

• Select the Specify maximum region size parameter.
• Use a detector that supports the Maximum Region Size parameter. For example, use a yolov2ObjectDetector object.

Programmatic Use

Minimum Region Size — Minimum region size

vector of the form [height _width_]

Specify the minimum region size as a vector of the form [height _width_]. Units are in pixels. The minimum region size defines the size of the smallest region containing the object. For example, [1 1] sets the size of the smallest region containing the object to1-by-1 pixels.

Dependencies

To enable this parameter

• Select the Specify minimum region size parameter.
• Use a detector that supports the Minimum Region Size parameter. For example, use a yolov2ObjectDetector object.

Programmatic Use

Maximum Number of Detections — Maximum number of detections

500 (default) | positive integer

Specify the maximum number of detections as a positive integer. This value is the upper bound for the number of detections.

Programmatic Use

Extended Capabilities

C/C++ Code Generation

Generate C and C++ code using Simulink® Coder™.

Usage notes and limitations:

• The Language parameter in the Configuration Parameters > Code Generation general category must be set to C++.
• For a list of networks and layers supported for code generation, see Networks and Layers Supported for Code Generation (MATLAB Coder).
• To generate code for the Math Kernel Library for Deep Neural Networks (MKL-DNN) target library with YOLOv3, YOLOv4, or RTMDet as the object detectors, set theMaxStackSize and TLCOptions model parameters to a higher value. For example, you can set the MaxStackSize andTLCOptions parameter values to 'inf' using the following syntax.

  set_param(modelName,'MaxStackSize','inf')
  set_param(modelName,'TLCOptions','-aMaxStackVariableSize=inf')

GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

• The Language parameter in the Configuration Parameters > Code Generation general category must be set to C++.
• For a list of networks and layers supported for CUDA® code generation, see Supported Networks, Layers, and Classes (GPU Coder).

Version History

Introduced in R2021b

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R2024b: Support to RTMDet object detector

Starting in R2024b, Deep Learning Object Detector block supports Real-Time Model for object Detection (RTMDet). You can import a pretrained RTMDet object detector object,rtmdetObjectDetector to perform object detection.

R2024b: Support for YOLOX object detector

Starting in R2024b, Deep Learning Object Detector block supports You Only Look Once X (YOLOX) object detection. You can import a pretrained YOLOX object detector object, yoloxObjectDetector to perform object detection.

See Also

Objects

• rcnnObjectDetector | fastRCNNObjectDetector | fasterRCNNObjectDetector | ssdObjectDetector | yolov2ObjectDetector | yolov3ObjectDetector | yolov4ObjectDetector | yoloxObjectDetector

Blocks

• Image Classifier (Deep Learning Toolbox) | Predict (Deep Learning Toolbox)