detect
Detect objects using YOLO v2 object detector configured for monocular camera
Syntax
Description
detects objects within image bboxes
= detect(detector
,I
)I
using you only look once version 2
(YOLO v2) object detector configured for a monocular camera. The locations of objects
detected are returned as a set of bounding boxes.
When using this function, use of a CUDA®-enabled NVIDIA® GPU is highly recommended. The GPU reduces computation time significantly. Usage of the GPU requires Parallel Computing Toolbox™. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).
[___,
returns a categorical array of labels assigned to the bounding boxes in addition to the
output arguments from the previous syntax. The labels used for object classes are defined
during training using the labels
] = detect(detector
,I
)trainYOLOv2ObjectDetector
function.
[___] = detect(___,
detects objects within the rectangular search region specified by
roi
)roi
. Use output arguments from any of the previous syntaxes. Specify
input arguments from any of the previous syntaxes.
detects objects within the series of images returned by the detectionResults
= detect(detector
,ds
)read
function
of the input datastore.
[___] = detect(___,
also specifies options using one or more Name,Value
)Name,Value
pair arguments in
addition to the input arguments in any of the preceding syntaxes.
Examples
Detect Vehicles in Traffic Scenes from Monocular Video Using YOLO v2
Configure a YOLO v2 object detector for detecting vehicles within a video captured by a monocular camera.
Load a yolov2ObjectDetector
object pretrained to detect vehicles.
vehicleDetector = load('yolov2VehicleDetector.mat','detector'); detector = vehicleDetector.detector;
Model a monocular camera sensor by creating a monoCamera
object. This object contains the camera intrinsics and the location of the camera on the ego vehicle.
focalLength = [309.4362 344.2161]; % [fx fy] principalPoint = [318.9034 257.5352]; % [cx cy] imageSize = [480 640]; % [mrows ncols] height = 2.1798; % Height of camera above ground, in meters pitch = 14; % Pitch of camera, in degrees intrinsics = cameraIntrinsics(focalLength,principalPoint,imageSize); sensor = monoCamera(intrinsics,height,'Pitch',pitch);
Configure the detector for use with the camera. Limit the width of detected objects to 1.5-2.5 meters. The configured detector is a yolov2ObjectDetectorMonoCamera
object.
vehicleWidth = [1.5 2.5]; detectorMonoCam = configureDetectorMonoCamera(detector,sensor,vehicleWidth);
Set up the video reader and read the input monocular video.
videoFile = '05_highway_lanechange_25s.mp4';
reader = VideoReader(videoFile);
Create a video player to display the video and the output detections.
videoPlayer = vision.DeployableVideoPlayer();
Detect vehicles in the video by using the detector. Specify the detection threshold as 0.6. Annotate the video with the bounding boxes for the detections, labels, and detection confidence scores.
cont = hasFrame(reader); while cont I = readFrame(reader); [bboxes,scores,labels] = detect(detectorMonoCam,I,'Threshold',0.6); % Run the YOLO v2 object detector if ~isempty(bboxes) displayLabel = strcat(cellstr(labels),':',num2str(scores)); I = insertObjectAnnotation(I,'rectangle',bboxes,displayLabel); end step(videoPlayer, I); cont = hasFrame(reader) && isOpen(videoPlayer); % Exit the loop if the video player figure window is closed end
Input Arguments
detector
— YOLO v2 object detector configured for monocular camera
yolov2ObjectDetectorMonoCamera
object
YOLO v2 object detector configured for monocular camera, specified as a yolov2ObjectDetectorMonoCamera
object. To create this object, use the
configureDetectorMonoCamera
function
with a monoCamera
object and trained yolov2ObjectDetector
object as inputs.
I
— Input image
H-by-W-by-C-by-B
numeric array of images
Input image, specified as an H-by-W-by-C-by-B numeric array of images. Images must be real, nonsparse, grayscale or RGB image.
H — Height in pixels.
W — Width in pixels
C — The channel size in each image must be equal to the network's input channel size. For example, for grayscale images, C must be equal to
1
. For RGB color images, it must be equal to3
.B — Number of images in the array.
The detector is sensitive to the range of the input image. Therefore, ensure that the input
image range is similar to the range of the images used to train the detector. For
example, if the detector was trained on uint8
images, rescale
this input image to the range [0, 255] by using the im2uint8
or rescale
function. The size of this input image should be comparable
to the sizes of the images used in training. If these sizes are very different, the
detector has difficulty detecting objects because the scale of the objects in the
input image differs from the scale of the objects the detector was trained to
identify. Consider whether you used the SmallestImageDimension
property during training to modify the size of training images.
Data Types: uint8
| uint16
| int16
| double
| single
| logical
ds
— Datastore
datastore
object
Datastore, specified as a datastore
object containing a
collection of images. Each image must be a grayscale, RGB, or multichannel image.
The function processes only the first column of the datastore, which must contain
images and must be cell arrays or tables with multiple columns.
roi
— Search region of interest
[x
y
width
height] vector
Search region of interest, specified as a four-element vector of the form [x y width height]. The vector specifies the upper left corner and size of a region in pixels.
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: detect(detector,I,'Threshold',0.25)
Threshold
— Detection threshold
0.5
(default) | scalar in the range [0, 1]
Detection threshold, specified as a comma-separated pair consisting of
'Threshold'
and a scalar in the range [0, 1]. Detections that
have scores less than this threshold value are removed. To reduce false positives,
increase this value.
SelectStrongest
— Select strongest bounding box
true
(default) | false
Select the strongest bounding box for each detected object, specified as the
comma-separated pair consisting of 'SelectStrongest'
and
true
or false
.
true
— Returns the strongest bounding box per object. The method calls theselectStrongestBboxMulticlass
function, which uses nonmaximal suppression to eliminate overlapping bounding boxes based on their confidence scores.By default, the
selectStrongestBboxMulticlass
function is called as followsselectStrongestBboxMulticlass(bbox,scores,... 'RatioType','Min',... 'OverlapThreshold',0.5);
false
— Return all the detected bounding boxes. You can then write your own custom method to eliminate overlapping bounding boxes.
MinSize
— Minimum region size
[1 1]
(default) | vector of the form [height
width]
Minimum region size, specified as the comma-separated pair consisting of
'MinSize'
and 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.
By default, 'MinSize'
is 1-by-1.
MaxSize
— Maximum region size
size
(I
) (default) | vector of the form [height
width]
Maximum region size, specified as the comma-separated pair consisting of
'MaxSize'
and 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.
By default, 'MaxSize'
is set to the height and width of the
input image, I
. To reduce computation time, set this value to the
known maximum region size for the objects that can be detected in the input test
image.
ExecutionEnvironment
— Hardware resource
'auto'
(default) | 'gpu'
| 'cpu'
Hardware resource on which to run the detector, specified as the comma-separated
pair consisting of 'ExecutionEnvironment'
and
'auto'
, 'gpu'
, or 'cpu'
.
'auto'
— Use a GPU if it is available. Otherwise, use the CPU.'gpu'
— Use the GPU. To use a GPU, you must have Parallel Computing Toolbox and a CUDA-enabled NVIDIA GPU. If a suitable GPU is not available, the function returns an error. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).'cpu'
— Use the CPU.
Acceleration
— Performance optimization
'auto'
(default) | 'mex'
| 'none'
Performance optimization, specified as the comma-separated pair consisting of
'Acceleration'
and one of the following:
'auto'
— Automatically apply a number of optimizations suitable for the input network and hardware resource.'mex'
— Compile and execute a MEX function. This option is available when using a GPU only. Using a GPU requires Parallel Computing Toolbox and a CUDA enabled NVIDIA GPU. If Parallel Computing Toolbox or a suitable GPU is not available, then the function returns an error. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).'none'
— Disable all acceleration.
The default option is 'auto'
. If 'auto'
is
specified, MATLAB® will apply a number of compatible optimizations. If you use the
'auto'
option, MATLAB does not ever generate a MEX function.
Using the 'Acceleration'
options 'auto'
and
'mex'
can offer performance benefits, but at the expense of an
increased initial run time. Subsequent calls with compatible parameters are faster.
Use performance optimization when you plan to call the function multiple times using
new input data.
The 'mex'
option generates and executes a MEX function based on
the network and parameters used in the function call. You can have several MEX
functions associated with a single network at one time. Clearing the network variable
also clears any MEX functions associated with that network.
The 'mex'
option is only available for input data specified as
a numeric array, cell array of numeric arrays, table, or image datastore. No other
types of datastore support the 'mex'
option.
The 'mex'
option is only available when you are using a GPU.
You must also have a C/C++ compiler installed. For setup instructions, see MEX Setup (GPU Coder).
'mex'
acceleration does not support all layers. For a list of
supported layers, see Supported Layers (GPU Coder).
Output Arguments
bboxes
— Location of objects detected
M-by-4 matrix | B-by-1 cell array
Location of objects detected within the input image or images, returned as an M-by-4 matrix or a B-by-1 cell array. M is the number of bounding boxes in an image, and B is the number of M-by-4 matrices when the input contains an array of images.
Each row of bboxes
contains a four-element vector of the
form [x
y
width
height]. This vector specifies the upper left corner and size
of that corresponding bounding box in pixels.
scores
— Detection scores
M-by-1 vector | B-by-1 cell array
Detection confidence scores, returned as an M-by-1 vector or a B-by-1 cell array. M is the number of bounding boxes in an image, and B is the number of M-by-1 vectors when the input contains an array of images. A higher score indicates higher confidence in the detection.
labels
— Labels for bounding boxes
M-by-1 categorical array | B-by-1 cell array
Labels for bounding boxes, returned as an M-by-1 categorical array or a
B-by-1 cell array. M is the number of
labels in an image, and B is the number of
M-by-1 categorical arrays when the input contains an
array of images. You define the class names used to label the objects when you
train the input detector
.
detectionResults
— Detection results
3-column table
Detection results, returned as a 3-column table with variable names, Boxes, Scores, and Labels. The Boxes column contains M-by-4 matrices, of M bounding boxes for the objects found in the image. Each row contains a bounding box as a 4-element vector in the format [x,y,width,height]. The format specifies the upper-left corner location and size in pixels of the bounding box in the corresponding image.
Version History
Introduced in R2019a
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