Main Content

detect

Detect objects using YOLO v3 object detector configured for monocular camera

Since R2023a

collapse all in page

Syntax

bboxes = detect(detector,I)
[bboxes,scores] = detect(detector,I)
[___,labels] = detect(detector,I)
[___] = detect(___,roi)
detectionResults = detect(detector,ds)
[___] = detect(___,Name=Value)

Description

example

bboxes = detect(detector,I) detects objects within image I using a you only look once version 3 (YOLO v3) object detector configured for a monocular camera. The function returns the locations of detected objects as a set of bounding boxes.

Using this function with CUDA®-enabled NVIDIA® GPU is highly recommended. A GPU reduces computation time significantly. Using a GPU requires Parallel Computing Toolbox™. For information about the supported compute capabilities, see GPU Computing Requirements (Parallel Computing Toolbox).

[bboxes,scores] = detect(detector,I) also returns the detection confidence scores for each bounding box.

[___,labels] = detect(detector,I) 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 creation of the detector.

[___] = detect(___,roi) detects objects within the rectangular search region specified by roi using input and output arguments from any of the previous syntaxes.

detectionResults = detect(detector,ds) detects objects within the series of images returned by the read function of the input datastore.

[___] = detect(___,Name=Value) also specifies options using one or more name-value arguments.

Examples

collapse all

This example uses:

Open Live Script

Configure a YOLO v3 object detector for use with a monocular camera mounted on an ego vehicle. Use this detector to detect vehicles and stop signs within an image captured by the camera.

Load a yolov3ObjectDetector object pretrained on the COCO data set.

detector = yolov3ObjectDetector;

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 yolov3ObjectDetectorMonoCamera object.

vehicleWidth = [1.5 2.5];
detectorMonoCam = configureDetectorMonoCamera(detector,sensor,vehicleWidth);

Read an image captured by the camera.

I = imread("object-detection-test.png");

Detect the vehicles and stop signs in the image by using the detector. Annotate the image with the bounding boxes for the detections and the class labels.

[bboxes,scores,labels] = detect(detectorMonoCam,I);
I = insertObjectAnnotation(I,"rectangle",bboxes,labels,AnnotationColor="green");
imshow(I)

Input Arguments

collapse all

YOLO v3 object detector configured for a monocular camera, specified as a yolov3ObjectDetectorMonoCamera object. To create this object, use the configureDetectorMonoCamera function with a monoCamera object and yolov3ObjectDetector object as inputs.

Input image, specified as an H-by-W-by-C-by-B numeric array of images. Images must be real, nonsparse, grayscale or RGB images.

  • H — Height in pixels.

  • W — Width in pixels.

  • C — The channel size in each image must be equal to the network input channel size. For example, for grayscale images, C must be equal to 1. For RGB color images, it must be equal to 3.

  • 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 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 input image differs from the scale of the objects the detector was trained to identify.

Data Types: uint8 | uint16 | int16 | double | single | logical

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.

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.

Example: detect(detector,I,Threshold=0.25)

Detection threshold, specified as a scalar in the range [0, 1]. The function removes detections that have scores less than this threshold value. To reduce false positives, increase this value.

Option to select the strongest bounding box for each detected object, specified as true or false.

  • true — Return the strongest bounding box per object using the selectStrongestBboxMulticlass function, which uses nonmaximal suppression to eliminate overlapping bounding boxes based on their confidence scores.

    By default, the selectStrongestBboxMulticlass function is called as follows:

     selectStrongestBboxMulticlass(bbox,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.

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.

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.

Minimum batch size, specified as a scalar. Specify the MiniBatchSize argument to process a large collection of images. Images are grouped into mini-batches and processed as a batch to improve computation efficiency. Use a large mini-batch size to decrease processing time. Use a small mini-batch size to use less memory.

Hardware resource on which to run the detector, specified as one of these values:

  • "auto" — Use a GPU if one 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.

Performance optimization, specified as one of these values:

  • "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 you use 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 "auto" and "mex" options 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 available only 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).

The "mex" acceleration does not support all layers. For a list of supported layers, see Supported Layers (GPU Coder).

Output Arguments

collapse all

Locations of detected objects 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 [x y width height]. This vector specifies the upper left corner and size of that corresponding bounding box in pixels.

Detection confidence scores, returned as an M-by-1 vector. M is the number of bounding boxes in an image. The detection score values lie between 0 and 1. A higher score indicates greater confidence in the detection.

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 detector.

Detection results, returned as a 3-column table with the 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 R2023a

See Also

Apps

Functions

Objects