Object Detection

Perform classification, object detection, transfer learning using convolutional neural networks (CNNs, or ConvNets), create customized detectors

Object detection is a computer vision technique for locating instances of objects in images or videos. Object detection algorithms typically leverage machine learning or deep learning to produce meaningful results. When looking at images or video, humans can recognize and locate objects of interest in a matter of moments. The goal of object detection is to replicate this intelligence using a computer. The best approach for object detection depends on your application and the problem you are trying to solve.

Deep learning techniques require a large number of labeled training images, so the use of a GPU is recommended to decrease the time needed to train a model. Deep learning-based approaches to object detection use convolutional neural networks (CNNs or ConvNets), such as R-CNN and YOLO, or use single-shot detection (SSD). You can train a custom object detector, or use a pretrained object detector by leveraging transfer learning, an approach that enables you to start with a pretrained network and then fine-tune it for your application. Convolutional neural networks require Deep Learning Toolbox™. Training and prediction are supported on a CUDA^®-capable GPU. Use of a GPU is recommended and requires Parallel Computing Toolbox™. For more information, see Computer Vision Toolbox Preferences and Parallel Computing Support in MathWorks Products (Parallel Computing Toolbox).

Machine learning techniques for object detection include aggregate channel features (ACF), support vector machines (SVM) classification using histograms of oriented gradient (HOG) features, and the Viola-Jones algorithm for human face or upper-body detection. You can choose to start with a pretrained object detector or create a custom object detector to suit your application.

Labeled boats, neural network, and person detector

Apps

Image Labeler	Label images for computer vision applications
Video Labeler	Label video for computer vision applications

Functions

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Detect Objects

Deep Learning Detectors

`rcnnObjectDetector`	Detect objects using R-CNN deep learning detector
`fastRCNNObjectDetector`	Detect objects using Fast R-CNN deep learning detector
`fasterRCNNObjectDetector`	Detect objects using Faster R-CNN deep learning detector
`ssdObjectDetector`	Detect objects using SSD deep learning detector
`yolov2ObjectDetector`	Detect objects using YOLO v2 object detector
`yolov3ObjectDetector`	Detect objects using YOLO v3 object detector
`yolov4ObjectDetector`	Detect objects using YOLO v4 object detector
`maskrcnn`	Detect objects using Mask R-CNN instance segmentation
`ocr`	Recognize text using optical character recognition

Feature-based Detectors

`readAprilTag`	Detect and estimate pose for AprilTag in image
`readBarcode`	Detect and decode 1-D or 2-D barcode in image
`acfObjectDetector`	Detect objects using aggregate channel features
`peopleDetectorACF`	Detect people using aggregate channel features
`vision.CascadeObjectDetector`	Detect objects using the Viola-Jones algorithm
`vision.ForegroundDetector`	Foreground detection using Gaussian mixture models
`vision.PeopleDetector`	Detect upright people using HOG features
`vision.BlobAnalysis`	Properties of connected regions

Detect Objects Using Point Features

`detectBRISKFeatures`	Detect BRISK features
`detectFASTFeatures`	Detect corners using FAST algorithm
`detectHarrisFeatures`	Detect corners using Harris–Stephens algorithm
`detectKAZEFeatures`	Detect KAZE features
`detectMinEigenFeatures`	Detect corners using minimum eigenvalue algorithm
`detectMSERFeatures`	Detect MSER features
`detectORBFeatures`	Detect ORB keypoints
`detectSIFTFeatures`	Detect scale invariant feature transform (SIFT) features
`detectSURFFeatures`	Detect SURF features
`extractFeatures`	Extract interest point descriptors
`matchFeatures`	Find matching features

Select Detected Objects

`selectStrongestBbox`	Select strongest bounding boxes from overlapping clusters
`selectStrongestBboxMulticlass`	Select strongest multiclass bounding boxes from overlapping clusters

Train Custom Object Detectors

Load Training Data

`boxLabelDatastore`	Datastore for bounding box label data
`groundTruth`	Ground truth label data
`imageDatastore`	Datastore for image data
`objectDetectorTrainingData`	Create training data for an object detector
`ocrTrainingOptions`	Options for training OCR model
`combine`	Combine data from multiple datastores

Train Feature-Based Object Detectors

`trainACFObjectDetector`	Train ACF object detector
`trainCascadeObjectDetector`	Train cascade object detector model
`trainImageCategoryClassifier`	Train an image category classifier

Train Deep Learning Based Object Detectors

`trainRCNNObjectDetector`	Train an R-CNN deep learning object detector
`trainFastRCNNObjectDetector`	Train a Fast R-CNN deep learning object detector
`trainFasterRCNNObjectDetector`	Train a Faster R-CNN deep learning object detector
`trainSSDObjectDetector`	Train an SSD deep learning object detector
`trainYOLOv2ObjectDetector`	Train YOLO v2 object detector
`trainYOLOv4ObjectDetector`	Train YOLO v4 object detector
`trainMaskRCNN`	Train Mask R-CNN network to perform instance segmentation
`ocrTrainingOptions`	Options for training OCR model
`trainOCR`	Train OCR model to recognize text in image
`quantizeOCR`	Quantize OCR model

Augment and Preprocess Training Data for Deep Learning

`balanceBoxLabels`	Balance bounding box labels for object detection
`bboxcrop`	Crop bounding boxes
`bboxerase`	Remove bounding boxes
`bboxresize`	Resize bounding boxes
`bboxwarp`	Apply geometric transformation to bounding boxes
`bbox2points`	Convert rectangle to corner points list
`imwarp`	Apply geometric transformation to image
`imcrop`	Crop image
`imresize`	Resize image
`randomAffine2d`	Create randomized 2-D affine transformation
`centerCropWindow2d`	Create rectangular center cropping window
`randomWindow2d`	Randomly select rectangular region in image
`integralImage`	Calculate 2-D integral image

Design Object Detection Deep Neural Networks

R-CNN (Regions With Convolutional Neural Networks)

`rcnnBoxRegressionLayer`	Box regression layer for Fast and Faster R-CNN
`fasterRCNNLayers`	Create a faster R-CNN object detection network
`rpnSoftmaxLayer`	Softmax layer for region proposal network (RPN)
`rpnClassificationLayer`	Classification layer for region proposal networks (RPNs)
`regionProposalLayer`	Region proposal layer for Faster R-CNN
`roiAlignLayer`	Non-quantized ROI pooling layer for Mask-CNN
`roiInputLayer`	ROI input layer for Fast R-CNN
`roiMaxPooling2dLayer`	Neural network layer used to output fixed-size feature maps for rectangular ROIs
`roialign`	Non-quantized ROI pooling of `dlarray` data

YOLO v2 (You Only Look Once version 2)

`yolov2Layers`	Create YOLO v2 object detection network
`yolov2TransformLayer`	Create transform layer for YOLO v2 object detection network
`yolov2OutputLayer`	Create output layer for YOLO v2 object detection network
`spaceToDepthLayer`	Space to depth layer

Focal Loss Layers

`focalLossLayer`	Create focal loss layer using focal loss function
`focalCrossEntropy`	Compute focal cross-entropy loss

SSD (Single Shot Detector)

ssdMergeLayer Create SSD merge layer for object detection

Anchor Boxes

estimateAnchorBoxes Estimate anchor boxes for deep learning object detectors

Visualize Detection Results

`cuboid2img`	Project cuboids from 3-D world coordinates to 2-D image coordinates
`insertObjectAnnotation`	Annotate truecolor or grayscale image or video stream
`insertObjectMask`	Insert masks in image or video stream
`insertShape`	Insert shapes in image or video
`showShape`	Display shapes on image, video, or point cloud

Evaluate Detection Results

`evaluateDetectionAOS`	Evaluate average orientation similarity metric for object detection
`evaluateDetectionMissRate`	Evaluate miss rate metric for object detection
`evaluateDetectionPrecision`	Evaluate precision metric for object detection
`bboxOverlapRatio`	Compute bounding box overlap ratio
`bboxPrecisionRecall`	Compute bounding box precision and recall against ground truth
`evaluateInstanceSegmentation`	Evaluate instance segmentation data set against ground truth
`evaluateOCR`	Evaluate OCR results against ground truth
`instanceSegmentationMetrics`	Instance segmentation quality metrics

Blocks

Deep Learning Object Detector

Detect objects using trained deep learning object detector

Topics

Get Started

Point Feature Types
Choose functions that return and accept points objects for several types of features.
Coordinate Systems
Specify pixel Indices, spatial coordinates, and 3-D coordinate systems
Local Feature Detection and Extraction
Learn the benefits and applications of local feature detection and extraction.
Choose Function to Visualize Detected Objects
Compare visualization functions.
Getting Started with Object Detection Using Deep Learning
Object detection using deep learning neural networks.
Getting Started with OCR
Detect and recognize text in multiple languages, train OCR models to recognize custom text.
Get Started with Cascade Object Detector
Train a custom classifier
Image Classification with Bag of Visual Words
Use the Computer Vision Toolbox™ functions for image category classification by creating a bag of visual words.

Training Data for Object Detection and Semantic Segmentation

Get Started with the Image Labeler
Interactively label rectangular ROIs for object detection, pixels for semantic segmentation, polygons for instance segmentation, and scenes for image classification.
Get Started with the Video Labeler
Interactively label rectangular ROIs for object detection, pixels for semantic segmentation, polygons for instance segmentation, and scenes for image classification in a video or image sequence.
Datastores for Deep Learning (Deep Learning Toolbox)
Learn how to use datastores in deep learning applications.
Getting Started with Mask R-CNN for Instance Segmentation
Perform multiclass instance segmentation using Mask R-CNN and deep learning.
Training Data for Object Detection and Semantic Segmentation
Create training data for object detection or semantic segmentation using the Image Labeler or Video Labeler.

Get Started With Deep Learning

Deep Network Designer (Deep Learning Toolbox)
List of Deep Learning Layers (Deep Learning Toolbox)
Discover all the deep learning layers in MATLAB^®.
Deep Learning in MATLAB (Deep Learning Toolbox)
Discover deep learning capabilities in MATLAB using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
Pretrained Deep Neural Networks (Deep Learning Toolbox)
Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction.