Main Content

detect

Detect objects using YOLO v3 object detector

Description

example

bboxes = detect(detector,I) detects objects within a single image or an array of images, I, using a you look only 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.

example

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

example

[bboxes,scores,labels] = detect(detector,I) returns a categorical array of labels assigned to the bounding boxes. The labels for object classes are defined during training.

example

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

example

[___] = detect(___,roi) detects objects within the rectangular search region roi, in addition to any combination of arguments from previous syntaxes.

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

Note

This function requires the Computer Vision Toolbox™ Model for YOLO v3 Object Detection. You can install the Computer Vision Toolbox Model for YOLO v3 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™.

Examples

collapse all

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 
    ________________________    ______    _______

    134     69    280    272     dog      0.56819

detectedImg = insertObjectAnnotation(img,'Rectangle',bboxes,labels);
figure
imshow(detectedImg)

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)

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.

results = table(bboxes,labels,scores)
results=1×3 table
            bboxes             labels    scores 
    _______________________    ______    _______

    99    103     41     33     car      0.72206

img = insertObjectAnnotation(img,'Rectangle',roiBox,'ROI','Color',"blue");
detectedImg = insertObjectAnnotation(img,'Rectangle',bboxes,labels);
figure
imshow(detectedImg)

Input Arguments

collapse all

YOLO v3 object detector, specified as a yolov3ObjectDetector object.

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 to 3.

  • 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

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.

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 comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

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

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.

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 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(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.

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.

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.

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.

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. The detect function calls the preprocess 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 either single or double.

Data Types: char | string

Output Arguments

collapse all

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.

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 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.

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 R2021a