Object Detection using Deep Learning

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

Convolutional neural networks (CNNs, or ConvNets) are essential tools for deep learning, and are especially useful for image classification, object detection, and recognition tasks. CNNs are implemented as a series of interconnected layers. The layers are made up of repeated blocks of convolutional, ReLU (rectified linear units), and pooling layers. The convolutional layers convolve their input with a set of filters. The filters were automatically learned during network training. The ReLU layer adds nonlinearity to the network, which enables the network to approximate the nonlinear mapping between image pixels and the semantic content of an image. The pooling layers downsample their inputs and help consolidate local image features.

Convolutional neural networks require Deep Learning Toolbox™. Training and prediction are supported on a CUDA®-capable GPU with a compute capability of 3.0 or higher. Use of a GPU is recommended and requires Parallel Computing Toolbox™.

You can construct a CNN architecture, train a network using semantic segmentation, and use the trained network to predict class labels or detect objects. You can also extract features from a pretrained network, and use these features to train a classifier. Additionally, you can perform transfer learning which retrains the CNN on new data.You can also use the Image Labeler, Video Labeler, feature extractors, and classifiers to create a custom detector.


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trainRCNNObjectDetectorTrain an R-CNN deep learning object detector
trainFastRCNNObjectDetectorTrain a Fast R-CNN deep learning object detector
trainFasterRCNNObjectDetectorTrain a Faster R-CNN deep learning object detector
trainYOLOv2ObjectDetectorTrain YOLO v2 object detector
rcnnObjectDetectorDetect objects using R-CNN deep learning detector
fastRCNNObjectDetectorDetect objects using Fast R-CNN deep learning detector
fasterRCNNObjectDetectorDetect objects using Faster R-CNN deep learning detector
yolov2ObjectDetectorDetect objects using YOLO v2 object detector
evaluateDetectionMissRateEvaluate miss rate metric for object detection
evaluateDetectionPrecisionEvaluate precision metric for object detection
bbox2pointsConvert rectangle to corner points list
bboxOverlapRatioCompute bounding box overlap ratio
bboxPrecisionRecallCompute bounding box precision and recall against ground truth
selectStrongestBboxSelect strongest bounding boxes from overlapping clusters
selectStrongestBboxMulticlassSelect strongest multiclass bounding boxes from overlapping clusters
roiInputLayerROI input layer for Fast R-CNN
roiMaxPooling2dLayerNeural network layer used to output fixed-size feature maps for rectangular ROIs
rpnSoftmaxLayerSoftmax layer for region proposal network (RPN)
rpnClassificationLayerClassification layer for region proposal networks (RPNs)
rcnnBoxRegressionLayerBox regression layer for Fast and Faster R-CNN
regionProposalLayerRegion proposal layer for Faster R-CNN
yolov2LayersCreate YOLO v2 object detection network
yolov2TransformLayerCreate transform layer for YOLO v2 object detection network
yolov2OutputLayerCreate output layer for YOLO v2 object detection network
yolov2ReorgLayerCreate reorganization layer for YOLO v2 object detection network


R-CNN, Fast R-CNN, and Faster R-CNN Basics

R-CNN, Fast R-CNN, and Faster R-CNN basics

YOLO v2 Basics

You only look once (YOLO) v2 basics

Anchor Boxes for Object Detection

Basics of anchor boxes that are used in deep learning object detection

Deep Network Designer

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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)

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Featured Examples