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Detect objects using SSD multibox object detector



bboxes = detect(detector,I) detects objects within a single image or an array of images, I, using an single shot multibox detector (SSD). The locations of objects detected are returned as a set of bounding boxes.

When using this function, use of a CUDA® enabled NVIDIA® GPU with a compute capability of 3.0 or higher is highly recommended. The GPU reduces computation time significantly. Usage of the GPU requires Parallel Computing Toolbox™.

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

[___,labels] = detect(detector,I) also returns a categorical array of labels assigned to the bounding boxes, using either of the preceding syntaxes. The labels used for object classes are defined during training using the trainSSDObjectDetector function.

[___] = detect(___,roi) detects objects within the rectangular search region specified by roi.

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

[___] = detect(___,Name,Value) specifies options using one or more Name,Value pair arguments. For example, detect(detector,I,'Threshold',0.75) sets the detection score threshold to 0.75. Any detections with a lower score are removed.


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Load a pretrained single shot detector (SSD) object to detect vehicles in an image. The detector is trained with images of cars on a highway scene.

vehicleDetector = load('ssdVehicleDetector.mat','detector');
detector = vehicleDetector.detector;

Read a test image into the workspace.

I = imread('highway.png');

Display the test image.


Run the pretrained SSD object detector by using the detect function. The output contains the bounding boxes, scores, and the labels for vehicles detected in the image. The labels are derived from the ClassNames property of the detector.

[bboxes,scores,labels] = detect(detector,I)
bboxes = 2×4

   139    78    96    81
    99    67   165   146

scores = 2x1 single column vector


labels = 1x2 categorical
     vehicle      vehicle 

Annotate the image with the detection results.

if ~isempty(bboxes)
    detectedI = insertObjectAnnotation(I,'rectangle',bboxes,cellstr(labels));
   detectedI = insertText(I,[10 10],'No Detections');

Input Arguments

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SSD object detector, specified as an ssdObjectDetector object. To create this object, call the trainSSDObjectDetector function with training data as input.

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.

  • 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.

  • 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 im2uint8 or 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 SmallestImageDimension property during training to modify the size of training images.

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 an [x y width height] vector. The vector specifies the upper left corner and size of a region in pixels.

Name-Value Pair 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: 'SelectStrongest',true

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.

Select the strongest bounding box for each detected object, specified as the comma-separated pair consisting of 'SelectStrongest' and either true or false.

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

    For example:

     selectStrongestBboxMulticlass(bbox,scores, ...
                'RatioType','Min', ...

  • false — Return all detected bounding boxes. You can then create your own custom operation to eliminate overlapping bounding boxes.

Maximum region size that contains a detected object, specified as the comma-separated pair consisting of 'MaxSize' and a [height width] vector. Units are in pixels.

To reduce computation time, set this value to the known maximum region size for the objects being detected in the image. By default, 'MaxSize' is set to the height and width of the input image, I.

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 images. 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.

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 with a compute capability of 3.0 or higher. If a suitable GPU is not available, the function returns an error.

  • 'cpu' — Use the CPU.

Output Arguments

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Location of objects detected within the input image or images, returned as an M-by-4 matrix or a B-by-1 cell array. M is the number of bounding boxes in an image, and B is the number of M-by-4 matrices when the input contains an array of images.

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 or a B-by-1 cell array. M is the number of bounding boxes in an image, and B is the number of M-by-1 vectors when the input contains an array of images. A higher score indicates higher confidence in the detection.

Labels for bounding boxes, returned as an M-by-1 categorical array or a B-by-1 cell array. M is the number of labels in an image, and B is the number of M-by-1 categorical arrays when the input contains an array of images. 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 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 R2020a