detect
Syntax
Description
detects objects within a single image or an array of images, bboxes
= detect(detector
,I
)I
, using
a you only look once version 4 (YOLO v4) object detector, detector
.
The detect
function automatically resizes and rescales the input image
to match that of the images used for training the detector. The locations of objects
detected in the input image are returned as a set of bounding boxes.
Note
To use the pretrained YOLO v4 object detection networks trained on COCO dataset, you must install the Computer Vision Toolbox™ Model for YOLO v4 Object Detection. You can download and install the Computer Vision Toolbox Model for YOLO v4 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™.
detects objects within all the images returned by the detectionResults
= detect(detector
,ds
)read
function of the input
datastore ds
.
[___] = detect(___,
detects objects within the rectangular search region roi
)roi
, in addition
to any combination of arguments from previous syntaxes.
[___] = detect(___,
specifies options using one or more name-value arguments, in addition to any combination
of arguments from previous syntaxes..Name=Value
)
Examples
Detect Objects Using Pretrained YOLO v4 Object Detector
Specify the name of a pretrained YOLO v4 deep learning network.
name = 'tiny-yolov4-coco';
Create YOLO v4 object detector by using the pretrained YOLO v4 network.
detector = yolov4ObjectDetector(name);
Detect objects in an unknown image by using the pretrained YOLO v4 object detector.
img = imread('sherlock.jpg');
img = im2single(imresize(img,0.5));
[bboxes,scores,labels] = detect(detector,img,Threshold=0.4)
bboxes = 1×4 single row vector
80.9433 31.6083 398.4628 288.3917
scores = single
0.4281
labels = categorical
dog
Display the detection results.
detectedImg = insertObjectAnnotation(img,'Rectangle',bboxes,labels);
figure
imshow(detectedImg)
Detect Objects in Image Datastore by Using YOLO v4 Detector
Load a pretrained YOLO v4 object detector.
detector = yolov4ObjectDetector("csp-darknet53-coco");
Read the test data and store as an image datastore object.
location = fullfile(matlabroot,'toolbox','vision','visiondata','vehicles'); imds = imageDatastore(location);
Detect objects in the test dataset. Set the Threshold
parameter value to 0.4 and MiniBatchSize
parameter value to 32.
detectionResults = detect(detector,imds,Threshold=0.4,MiniBatchSize=32);
Read an image from the test dataset and extract the corresponding detection results.
num = 20; I = readimage(imds,num); bboxes = detectionResults.Boxes{num}; labels = detectionResults.Labels{num}; scores = detectionResults.Scores{num};
Perform non-maximal suppression to select strongest bounding boxes from the overlapping clusters. Set the OverlapThreshold
parameter value to 0.5.
[bboxes,scores,labels] = selectStrongestBboxMulticlass(bboxes,...
scores,labels,OverlapThreshold=0.5);
Display the detection results.
results = table(bboxes,labels,scores)
results=2×3 table
bboxes labels scores
____________________________________ ______ _______
17.818 69.966 23.459 11.381 car 0.90267
75.206 66.011 26.134 23.541 car 0.58296
detectedImg = insertObjectAnnotation(I,"Rectangle",bboxes,labels);
figure
imshow(detectedImg)
Detect Objects Within ROI by Using YOLO v4 Detector
Load a pretrained YOLO v4 object detector.
detector = yolov4ObjectDetector("csp-darknet53-coco");
Read a test image.
img = imread("stopsign.jpg");
Specify a region of interest (ROI) within the test image.
roiBox = [250 60 500 300];
Detect objects within the specified ROI.
[bboxes,scores,labels] = detect(detector,img,roiBox);
Display the ROI and the detection results.
img = insertObjectAnnotation(img,"Rectangle",roiBox,"ROI",AnnotationColor="blue"); detectedImg = insertObjectAnnotation(img,"Rectangle",bboxes,labels); figure imshow(detectedImg)
Input Arguments
detector
— YOLO v4 object detector
yolov4ObjectDetector
object
YOLO v4 object detector, specified as a yolov4ObjectDetector
object.
I
— Test images
numeric array
Test images, specified as a numeric array of size H-by-W-byC or H-by-W-byC-by-T. Images must be real, nonsparse, grayscale or RGB image.
H: Height
W: Width
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
.T: Number of test images in the array. The function computes the object detection results for each test image in the array.
Data Types: uint8
| uint16
| int16
| double
| single
ds
— Test images
ImageDatastore
object | CombinedDatastore
object | TransformedDatastore
object
Test images, specified as a ImageDatastore
object,
CombinedDatastore
object, or
TransformedDatastore
object containing full filenames of the test
images. The images in the datastore must be grayscale, or RGB images.
roi
— Search region of interest
[x
y
width
height] vector
Search region of interest, specified as an [x y width height] vector. 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.
Example: detect(detector,I,Threshold=0.25)
Threshold
— Detection threshold
0.5
(default) | scalar in the range [0, 1]
Detection threshold, specified as 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
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(bboxes,scores,... RatioType="Union",... 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 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 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.
MiniBatchSize
— Minimum batch size
128
(default) | scalar
Minimum batch size, specified as a scalar value. Use the
MiniBatchSize
to process a large collection of image. Images
are grouped into minibatches and processed as a batch to improve computation
efficiency. Increase the minibatch size to decrease processing time. Decrease the size
to use less memory.
ExecutionEnvironment
— Hardware resource
"auto"
(default) | "gpu"
| "cpu"
Hardware resource on which to run the detector, specified as
"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 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® applies 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 | M-by-5 matrix | T-by-1 cell array
Location of objects detected within the input image or images, returned as a
M-by-4 matrix or an M-by-5 matrix if the input is a single test image.
T-by-1 cell array if the input is an array of test images. T is the number of test images in the array. M is the number of bounding boxes in an image
The table describes the format of bounding boxes.
Bounding Box | Description |
---|---|
rectangle |
Defined in spatial coordinates as an M-by-4 numeric matrix with rows of the form [x y w h], where:
|
rotated-rectangle |
Defined in spatial coordinates as an M-by-5 numeric matrix with rows of the form [xctr yctr xlen ylen yaw], where:
|
scores
— Detection scores
M-by-1 numeric vector | T-by-1 cell array
Detection confidence scores for each bounding box, returned as one of these options:
M-by-1 numeric vector — The input is a single test image. M is the number of bounding boxes detected in the image.
B-by-1 cell array — The input is a batch of test images, where B is the number of test images in the batch. Each cell in the array contains an M-element row vector, where each element indicates the detection score for a bounding box in the corresponding image.
A higher score indicates higher confidence in the detection. The confidence score for each detection is a product of the corresponding objectness score and maximum class probability. The objectness score is the probability that the object in the bounding box belongs to a class in the image. The maximum class probability is the largest probability that a detected object in the bounding box belongs to a particular class.
labels
— Labels for bounding boxes
M-by-1 categorical vector | T-by-1 cell array
Labels for bounding boxes, returned as one of these options:
M-by-1 categorical vector if the input is a single test image.
T-by-1 cell array if the input is an array of test images. T is the number of test images in the array. Each cell in the array contains a M-by-1 categorical vector containing the names of the object classes.
M is the number of bounding boxes detected in an image.
detectionResults
— Detection results
3-column table
Detection results, returned as a 3-column table with variable names, Boxes, Scores, and Labels. The Boxes column can contain rectangles or rotated rectangle bounding boxes of the form :
rectangle — M-by-4 matrices, of M bounding boxes for the objects found in the image. Each row specifies a rectangle as a 4-element vector of the form [x,y,width,height], where (x,y) specifies the upper-left corner location and (width, height) specifies the size in pixels
rotated rectangle — M-by-5 matrices of M bounding boxes for the objects found in the image. Each row specifies a rotated rectangle as a 5-element vector of the form [xctr,yctr,width, height,yaw], where (xctr,yctr) specifies the center, (width,height) specifies the size, and yaw specifies the rotated angle.
info
— Class probabilities and objectness scores
structure array
Class probabilities and objectness scores of the detections, returned as a structure array with these fields.
ClassProbabilities
— Class probabilities for each of the detections, returned as a B-by-1 cell array. B is the number of images in the input batch of images,I
. Each cell in the array contains the class probabilities as an M-by-N numeric matrix. M is the number of bounding boxes and N is the number of classes. Each class probability is a numeric scalar, indicating the probability that the detected object in the bounding box belongs to a class in the image.ObjectnessScores
— Objectness scores for each of the detections, returned as a B-by-1 cell array. B is the number of images in the input batch of images,I
. Each cell in the array contains the objectness score for each bounding box as an M-by-1 numeric vector. M is the number of bounding boxes. Each objectness score is a numeric scalar, indicating the probability that the bounding box contains an object belonging to one of the classes in the image.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
The
roi
argument to thedetect
method must be a code generation constant (coder.const()
) and a 1x4 vector.Only the
Threshold
,SelectStrongest
,MinSize
,MaxSize
, andMiniBatchSize
name-value pairs fordetect
are supported.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
The
roi
argument to thedetect
method must be a code generation constant (coder.const()
) and a 1x4 vector.Only the
Threshold
,SelectStrongest
,MinSize
,MaxSize
, andMiniBatchSize
name-value pairs fordetect
are supported.
For information about how to create a yolov4ObjectDetector
object for
code generation, see Load Pretrained Networks for Code Generation (MATLAB Coder).
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Usage notes and limitations:
GPU Arrays is not supported for rotated rectangle bounding box inputs.
Version History
Introduced in R2022aR2024a: Option to return class probabilities and objectness scores
Specify the info
output
argument to return information about the class probability and objectness score for each
detection.
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