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 3 (YOLO v3) object detector, detector
.
The input size of the image must be greater than or equal to the network input size of the
pretrained detector. The locations of objects detected are returned as a set of bounding
boxes.
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.Name,Value
)
Note
To run this function, you will require the Deep Learning Toolbox™.
Examples
Detect Objects Using YOLO v3 Object Detector
Load a pretrained YOLO v3 object detector.
detector = yolov3ObjectDetector('tiny-yolov3-coco');
Read a test image and preprocess the test image by using the preprocess
function.
img = imread('sherlock.jpg');
img = preprocess(detector,img);
Detect objects in the test image.
[bboxes,scores,labels] = detect(detector,img);
Display the detection results.
results = table(bboxes,labels,scores)
results=1×3 table
bboxes labels scores
________________________ ______ _______
133 67 283 278 dog 0.51771
detectedImg = insertObjectAnnotation(img,'Rectangle',bboxes,labels);
figure
imshow(detectedImg)
Detect Objects from Images Stored in Image Datastore
Load a pretrained YOLOv3 object detector.
detector = yolov3ObjectDetector('tiny-yolov3-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 0.3 and MiniBatchSize
parameter value to 32.
detectionResults = detect(detector,imds,'Threshold',0.3,'MiniBatchSize',32);
Read an image from the test dataset and extract the corresponding detection results.
num = 10; 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 0.2.
[bboxes,scores,labels] = selectStrongestBboxMulticlass(bboxes,scores,labels,'OverlapThreshold',0.2);
Display the detection results.
results = table(bboxes,labels,scores)
results=3×3 table
bboxes labels scores
________________________ ______ _______
14 71 52 27 car 0.93352
74 73 7 5 car 0.65369
102 73 15 10 car 0.85313
detectedImg = insertObjectAnnotation(I,'Rectangle',bboxes,labels);
figure
imshow(detectedImg)
Detect Objects Within ROI
Load a pretrained YOLO v3 object detector.
detector = yolov3ObjectDetector('tiny-yolov3-coco');
Read a test image.
img = imread('highway.png');
Specify a region of interest (ROI) within the test image.
roiBox = [70 40 100 100];
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','Color',"blue"); detectedImg = insertObjectAnnotation(img,'Rectangle',bboxes,labels); figure imshow(detectedImg)
Input Arguments
detector
— YOLO v3 object detector
yolov3ObjectDetector
object
YOLO v3 object detector, specified as a yolov3ObjectDetector
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.
The intensity range of the test image must be similar to the intensity range of the
images used to train the detector. For example, if you train the detector on
uint8
images, rescale the test image to the range [0, 255] by using
the im2uint8
or rescale
function. The size of the test image must 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 test image differs from the scale of the
objects the detector was trained to identify.
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.
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 either
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 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.
MiniBatchSize
— Minimum batch size
128
(default) | scalar
Minimum batch size, specified as the comma-separated pair consisting of
'MiniBatchSize'
and 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 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® 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).
DetectionPreprocessing
— Option to preprocess test images
'auto'
(default) | 'none'
Option to preprocess the test images before performing object detection, specified
as the comma-separated pair consisting of 'DetectionPreprocessing'
and one of these values:
'auto'
— To preprocess the test image before performing object detection. Thedetect
function calls thepreprocess
function that perform these operations:Rescales the intensity values of the training images to the range [0, 1].
Resizes the training images to one of the nearest network input sizes and updates the bounding box coordinate values for accurate training. The function preserves the original aspect ratio of the training data.
'none'
— To perform object detection without preprocessing the test image. If you choose this option, the datatype of the test image must be eithersingle
ordouble
.
Data Types: char
| string
Output Arguments
bboxes
— Location of objects detected
M-by-4 matrix | T-by-1 cell array
Location of objects detected within the input image or images, returned as a
M-by-4 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. Each cell in the array contains a M-by-4 matrix specifying the bounding box detections.
. M is the number of bounding boxes in an image.
Each row in the matrix is 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- element row vector | T-by-1 cell array
Detection confidence scores for each bounding box, returned as a
M- element row 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-element row vector indicating the detection scores for the corresponding bounding box.
M is the number of bounding boxes detected in an image. A higher score indicates higher confidence in the detection.
labels
— Labels for bounding boxes
M-by-1 categorical vector | T-by-1 cell array
Labels for bounding boxes, returned as a
M-by-1 categorical array 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 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.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
The
roi
argument to thedetect
method must be a code generation constant (coder.const()
) and a 1x4 vector.Only the
Threshold
,SelectStrongest
,MinSize
, andMaxSize
name-value pairs fordetect
are supported.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
The
roi
argument to thedetect
method must be a codegen constant (coder.const()
) and a 1x4 vector.Only the
Threshold
,SelectStrongest
,MinSize
, andMaxSize
name-value pairs are supported.The height, width, and channel of the input image must be fixed size.
Version History
Introduced in R2021a
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