Detect objects using YOLO v4 object detector configured for monocular camera
detects objects within image
bboxes = detect(
I using you only look once version 4
(YOLO v4) 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).
[___] = detect(___,
detects objects within the rectangular search region specified by
roi. Use output arguments from any of the previous syntaxes. Specify
input arguments from any of the previous syntaxes.
[___] = detect(___,
also specifies options using one or more
Name,Value pair arguments in
addition to the input arguments in any of the preceding syntaxes.
Detect Cars Using Monocular Camera and YOLO v4
Load a pretrained
detector = yolov4ObjectDetector("csp-darknet53-coco");
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
vehicleWidth = [1.5 2.5]; detectorMonoCam = configureDetectorMonoCamera(detector,sensor,vehicleWidth);
Read in an image captured by the camera.
I = imread("carsinfront.png");
Detect the cars in the image by using the detector. Annotate the image with the bounding boxes for the detections and the detection confidence scores.
[bboxes,scores,labels] = detect(detectorMonoCam,I); I = insertObjectAnnotation(I,"Rectangle",bboxes,scores,Color="yellow"); imshow(I)
Display the labels for detected bounding boxes. The labels specify the class names of the detected objects.
car car car car car
detector — YOLO v4 object detector configured for monocular camera
I — Input image
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.
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 to
B: The 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
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
property during training to modify the size of training images.
ds — Datastore
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
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.
Specify optional pairs of arguments as
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.
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) |
Select the strongest bounding box for each detected object, specified as the
comma-separated pair consisting of
true— Returns the strongest bounding box per object. The method calls the
selectStrongestBboxMulticlassfunction, which uses nonmaximal suppression to eliminate overlapping bounding boxes based on their confidence scores.
By default, the
selectStrongestBboxMulticlassfunction is called as follows
selectStrongestBboxMulticlass(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
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.
'MinSize' is 1-by-1.
MaxSize — Maximum region size
I) (default) | vector of the form [height
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.
'MaxSize' is set to the height and width of the
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
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) |
Hardware resource on which to run the detector, specified as the comma-separated
pair consisting of
'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) |
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
specified, MATLAB® will apply a number of compatible optimizations. If you use the
'auto' option, MATLAB does not ever generate a MEX function.
'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.
'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.
'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 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).
bboxes — Location of objects detected
Location of objects detected within the input image or images, returned as an M-by-4 matrix. M is the number of bounding boxes in an image.
Each row of
bboxes contains a four-element vector of the form
height]. This vector specifies the upper left corner and size of that
corresponding bounding box in pixels.
scores — Detection scores
Detection confidence scores, returned as an M-by-1 vector. M is the number of bounding boxes in an image. The value of detection score lies between 0 and 1. A higher score indicates higher confidence in the detection.
labels — Labels for bounding boxes
M-by-1 categorical array
Labels for bounding boxes, returned as an M-by-1 categorical
array. M is the number of labels in an image. You define the class
names used to label the objects when you train the input
detectionResults — Detection results
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.
Introduced in R2022a