ssdObjectDetectorMonoCamera

Detect objects in monocular camera using SSD deep learning detector

Description

The ssdObjectDetectorMonoCamera detects objects from an image, using a single shot detector (SSD) object detector. To detect objects in an image, pass the trained detector to the detect (Computer Vision Toolbox) function.

Creation

1. Create a ssdObjectDetector (Computer Vision Toolbox) object by calling the trainSSDObjectDetector (Computer Vision Toolbox) function with training data (requires Deep Learning Toolbox™).

   detector = trainSSDObjectDetector(trainingData,____);

2. Create a monoCamera object to model the monocular camera sensor.

   sensor = monoCamera(____);

3. Create a ssdObjectDetectorMonoCamera object by passing the detector and sensor as inputs to the configureDetectorMonoCamera function. The configured detector inherits property values from the original detector.

   configuredDetector = configureDetectorMonoCamera(detector,sensor,____);

Properties

Camera — Camera configuration

monoCamera object

This property is read-only.

Camera configuration, specified as a monoCamera object. The object contains the camera intrinsics, the location, the pitch, yaw, and roll placement, and the world units for the parameters. Use the intrinsics to transform the object points in the image to world coordinates, which you can then compare to the values in the WorldObjectSize property.

WorldObjectSize — Range of object widths and lengths

[minWidth maxWidth] vector | [minWidth maxWidth; minLength maxLength] vector

Range of object widths and lengths in world units, specified as a [minWidth maxWidth] vector or [minWidth maxWidth; minLength maxLength] vector. Specifying the range of object lengths is optional.

ModelName — Name of classification model

character vector | string scalar

Name of the classification model, specified as a character vector or string scalar. You can modify this name after creating the ssdObjectDetectorMonoCamera object.

Network — Trained SSD object detection network

DAGNetwork object

This property is read-only.

Trained SSD object detection network, specified as a DAGNetwork (Deep Learning Toolbox) object. This object stores the layers that are used within the SSD object detector.

AnchorBoxes — Size of anchor boxes

P-by-1 cell array

This property is read-only.

Size of anchor boxes, specified as a P-by-1 cell array for P number of feature extraction layers used for object detection in the SSD network. Each element of the array contains an M-by-2 matrix of anchor box sizes, in the format [height width]. Each cell can contain a different number of anchor boxes. This value is set during training.

ClassNames — Names of object classes

cell array of character vectors

This property is read-only.

Names of the object classes that the SSD object detector was trained to find, specified as a cell array of character vectors. This property is set by the trainingData input argument for the trainSSDObjectDetector (Computer Vision Toolbox) function. Specify the class names as part of the trainingData table.

Object Functions

detect (Computer Vision Toolbox)

Detect objects using SSD multibox object detector

Examples

Detect Vehicles Using Monocular Camera and SSD

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

Load a ssdObjectDetector object pretrained to detect vehicles.

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

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 ssdObjectDetectorMonoCamera object.

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

Read an image captured by the camera.

I = imread('highwayCars.png');

Detect the vehicles 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,'Threshold',0.6);
I = insertObjectAnnotation(I,'rectangle',bboxes,scores,'Color','g');
imshow(I)

Display the labels for detected bounding boxes. The labels specify the class names of the detected objects.

disp(labels)

     vehicle      vehicle