Computer vision

Estendere i workflow di Deep Learning con le applicazioni di computer vision

Applicare il Deep Learning alle applicazioni di computer vision utilizzando Deep Learning Toolbox™ insieme a Computer Vision Toolbox™.

App

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

Funzioni

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Datastore per i dati di addestramento

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

ViT (Vision Transformer)

`visionTransformer`	Pretrained vision transformer (ViT) neural network (Da R2023b)
`patchEmbeddingLayer`	Patch embedding layer (Da R2023b)

Segmentazione semantica

`unet`	Create U-Net convolutional neural network for semantic segmentation (Da R2024a)
`unet3d`	Create 3-D U-Net convolutional neural network for semantic segmentation of volumetric images (Da R2024a)
`deeplabv3plus`	Create DeepLab v3+ convolutional neural network for semantic image segmentation (Da R2024a)

Rilevamento dell'oggetto e segmentazione dell'istanza

`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 (Da R2020a)
`yolov2ObjectDetector`	Detect objects using YOLO v2 object detector
`yolov3ObjectDetector`	Detect objects using YOLO v3 object detector (Da R2021a)
`yolov4ObjectDetector`	Detect objects using YOLO v4 object detector (Da R2022a)

Segmentazione dell'istanza

`solov2`	Segment objects using SOLOv2 instance segmentation network (Da R2023b)
`maskrcnn`	Detect objects using Mask R-CNN instance segmentation (Da R2021b)

Stima della posa

posemaskrcnn Predict object pose using Pose Mask R-CNN pose estimation (Da R2024a)

Tracking e re-identificazione dell'oggetto

reidentificationNetwork Re-identification deep learning network for re-identifying and tracking objects (Da R2024a)

Ispezione visiva automatizzata

`fcddAnomalyDetector`	Detect anomalies using fully convolutional data description (FCDD) network for anomaly detection (Da R2022b)
`fastFlowAnomalyDetector`	Detect anomalies using FastFlow network (Da R2023a)
`patchCoreAnomalyDetector`	Detect anomalies using PatchCore network (Da R2023a)
`yoloxObjectDetector`	Detect objects using YOLOX object detector (Da R2023b)

Rilevamento e riconoscimento del testo

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

Argomenti

Classificazione dell'immagine

Train Vision Transformer Network for Image Classification
This example shows how to fine-tune a pretrained vision transformer (ViT) neural network neural network to perform classification on a new collection of images.

Rilevamento dell'oggetto e segmentazione dell'istanza

Getting Started with Object Detection Using Deep Learning (Computer Vision Toolbox)
Perform object detection using deep learning neural networks.
Get Started with Instance Segmentation Using Deep Learning (Computer Vision Toolbox)
Segment objects using an instance segmentation model such as SOLOv2 or Mask R-CNN.
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.

Ispezione visiva automatizzata

Getting Started with Anomaly Detection Using Deep Learning (Computer Vision Toolbox)
Anomaly detection using deep learning is an increasingly popular approach to automating visual inspection tasks.
Detect Image Anomalies Using Explainable FCDD Network
Use an anomaly detector to distinguish between normal pills and pills with anomalous chips or contamination.
Classify Defects on Wafer Maps Using Deep Learning
This example shows how to classify eight types of manufacturing defects on wafer maps using a simple convolutional neural network (CNN).
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.
Localize Industrial Defects Using PatchCore Anomaly Detector
Perform localization of anomalous defects in printed circuit boards (PCBs) using anomaly heat maps generated with the PatchCore anomaly detector.

Segmentazione semantica

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).
Prune and Quantize Semantic Segmentation Network
Reduce the memory footprint of a semantic segmentation network and speed-up inference by compressing the network using pruning and quantization.

Classificazione video

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.

Esempi in primo piano

Detect Defects on Printed Circuit Boards Using YOLOX Network

Detect, localize, and classify defects in printed circuit boards (PCBs) using a you only look once version X (YOLOX) deep learning network.

Apri live script

Perform 6-DoF Pose Estimation for Bin Picking Using Deep Learning

Perform six degrees-of-freedom (6-DoF) pose estimation by estimating the 3-D position and orientation of machine parts in a bin using RGB-D images and a deep learning network.

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Perform Instance Segmentation Using SOLOv2

Segment object instances of randomly rotated machine parts in a bin using a deep learning SOLOv2 network.

Object Detection Using YOLO v2 Deep Learning

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

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Point Cloud Classification Using PointNet Deep Learning

Train a PointNet network for point cloud classification.

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Object Detection Using SSD Deep Learning

Train a Single Shot Detector (SSD).

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Object Detection Using YOLO v3 Deep Learning

Train a YOLO v3 object detector using custom training loop.

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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

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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).

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Semantic Segmentation Using Deep Learning

Segment an image using a semantic segmentation network.

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Generate Image from Segmentation Map Using Deep Learning

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

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Estimate Body Pose Using Deep Learning

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

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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].

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Gesture Recognition using Videos and Deep Learning

Perform gesture recognition using a pretrained SlowFast video classifier.

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Reidentify People Throughout a Video Sequence Using ReID Network

Track people throughout a video sequence using re-identification with a residual network.

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