La traduction de cette page n'est pas à jour. Cliquez ici pour voir la dernière version en anglais.

Computer Vision avec Deep Learning

Enrichir des workflows de Deep Learning avec des applications de Computer Vision

Appliquez le Deep Learning à des applications de Computer Vision en utilisant Deep Learning Toolbox™ avec Computer Vision Toolbox™.

Applications

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

Fonctions

développer tout

Datastores de données d’apprentissage

`boxLabelDatastore`	Datastore for bounding box label data (depuis R2019b)
`pixelLabelDatastore`	Datastore for pixel label data

Segmentation sémantique

`deeplabv3plusLayers`	(To be removed) Create DeepLab v3+ convolutional neural network for semantic image segmentation (depuis R2019b)
`segnetLayers`	(To be removed) Create SegNet layer graph for semantic segmentation
`unetLayers`	(To be removed) Create U-Net layers for semantic segmentation
`unet3dLayers`	(To be removed) Create 3-D U-Net layers for semantic segmentation of volumetric images (depuis R2019b)

Détection d’objets

`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 (depuis R2020a)
`yolov2ObjectDetector`	Detect objects using YOLO v2 object detector
`yolov3ObjectDetector`	Detect objects using YOLO v3 object detector (depuis R2021a)
`yolov4ObjectDetector`	Detect objects using YOLO v4 object detector (depuis R2022a)
`fcddAnomalyDetector`	Detect anomalies using fully convolutional data description (FCDD) network for anomaly detection (depuis R2022b)
`maskrcnn`	Detect objects using Mask R-CNN instance segmentation (depuis R2021b)

Détection et reconnaissance de texte

detectTextCRAFT Detect texts in images by using CRAFT deep learning model (depuis R2022a)

Rubriques

Détection d’objets

Getting Started with Object Detection Using Deep Learning (Computer Vision Toolbox)
Perform object detection using deep learning neural networks.
Augment Bounding Boxes for Object Detection
This example shows how to perform common kinds of image and bounding box augmentation as part of object detection workflows.
Train Object Detector Using R-CNN Deep Learning
This example shows how to train an object detector using deep learning and R-CNN (Regions with Convolutional Neural Networks).
Import Pretrained ONNX YOLO v2 Object Detector
This example shows how to import a pretrained ONNX™ (Open Neural Network Exchange) you only look once (YOLO) v2 [1] object detection network and use it to detect objects.
Export YOLO v2 Object Detector to ONNX
This example shows how to export a YOLO v2 object detection network to ONNX™ (Open Neural Network Exchange) model format.
Deploy Object Detection Model as Microservice (MATLAB Compiler SDK)
Use a microservice to detect objects in images.

Segmentation sémantique

Getting Started with Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
Segment objects by class using deep learning.
Augment Pixel Labels for Semantic Segmentation
This example shows how to perform common kinds of image and pixel label augmentation as part of semantic segmentation workflows.
Semantic Segmentation Using Dilated Convolutions
Train a semantic segmentation network using dilated convolutions.
Semantic Segmentation of Multispectral Images Using Deep Learning
This example shows how to perform semantic segmentation of a multispectral image with seven channels using U-Net.
3-D Brain Tumor Segmentation Using Deep Learning
This example shows how to perform semantic segmentation of brain tumors from 3-D medical images.
Explore Semantic Segmentation Network Using Grad-CAM
This example shows how to explore the predictions of a pretrained semantic segmentation network using Grad-CAM.
Generate Adversarial Examples for Semantic Segmentation (Computer Vision Toolbox)
Generate adversarial examples for a semantic segmentation network using the basic iterative method (BIM).

Classification de vidéos

Activity Recognition from Video and Optical Flow Data Using Deep Learning
This example first shows how to perform activity recognition using a pretrained Inflated 3-D (I3D) two-stream convolutional neural network based video classifier and then shows how to use transfer learning to train such a video classifier using RGB and optical flow data from videos [1].
Gesture Recognition using Videos and Deep Learning
Perform gesture recognition using a pretrained SlowFast video classifier.

Exemples présentés

Object Detection Using Faster R-CNN Deep Learning

Train a Faster R-CNN (regions with convolutional neural networks) object detector.

Ouvrir le live script

Object Detection Using YOLO v2 Deep Learning

Train a you only look once (YOLO) v2 object detector.

Ouvrir le live script

Point Cloud Classification Using PointNet Deep Learning

Train a PointNet network for point cloud classification.

Ouvrir le live script

Object Detection Using SSD Deep Learning

Train a Single Shot Detector (SSD).

Ouvrir le live script

Object Detection Using YOLO v3 Deep Learning

Train a YOLO v3 object detector using custom training loop.

Ouvrir le live script

Object Detection Using YOLO v4 Deep Learning

Detect objects in images using you only look once version 4 (YOLO v4) deep learning network. In this example, you will

Ouvrir le live script

Perform Instance Segmentation Using Mask R-CNN

Segment individual instances of people and cars using a multiclass mask region-based convolutional neural network (R-CNN).

Ouvrir le live script

Semantic Segmentation Using Deep Learning

Segment an image using a semantic segmentation network.

Ouvrir le live script

Generate Image from Segmentation Map Using Deep Learning

Generate a synthetic image of a scene from a semantic segmentation map.

Ouvrir le live script

Estimate Body Pose Using Deep Learning

Estimate the body pose of one or more people using the OpenPose algorithm.

Ouvrir le live script

Activity Recognition from Video and Optical Flow Data Using Deep Learning

First shows how to perform activity recognition using a pretrained Inflated 3-D (I3D) two-stream convolutional neural network based video classifier and then shows how to use transfer learning to train such a video classifier using RGB and optical flow data from videos [1].

Ouvrir le live script

Gesture Recognition using Videos and Deep Learning

Perform gesture recognition using a pretrained SlowFast video classifier.

Ouvrir le live script

Detect Image Anomalies Using Explainable FCDD Network

Use an anomaly detector to distinguish between normal pills and pills with anomalous chips or contamination.

Ouvrir le live script

Classify Defects on Wafer Maps Using Deep Learning

Classify eight types of manufacturing defects on wafer maps using a simple convolutional neural network (CNN).

Ouvrir le live script

Detect Image Anomalies Using Pretrained ResNet-18 Feature Embeddings

Train a similarity-based anomaly detector using one-class learning of feature embeddings extracted from a pretrained ResNet-18 convolutional neural network.

Ouvrir le live script