计算机视觉

利用计算机视觉应用扩展深度学习工作流

通过将 Computer Vision Toolbox™ 与 Deep Learning Toolbox™ 结合使用，将深度学习应用于计算机视觉应用。

App

图像标注器	Label images for computer vision applications
视频标注器	Label video for computer vision applications

函数

全部展开

训练数据的数据存储

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

语义分割

`deeplabv3plusLayers`	Create DeepLab v3+ convolutional neural network for semantic image segmentation （自 R2019b 起）
`segnetLayers`	Create SegNet layers for semantic segmentation
`unetLayers`	Create U-Net layers for semantic segmentation
`unet3dLayers`	Create 3-D U-Net layers for semantic segmentation of volumetric images （自 R2019b 起）

目标检测

`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 （自 R2020a 起）
`yolov2ObjectDetector`	Detect objects using YOLO v2 object detector （自 R2019a 起）
`yolov3ObjectDetector`	Detect objects using YOLO v3 object detector （自 R2021a 起）
`yolov4ObjectDetector`	Detect objects using YOLO v4 object detector （自 R2022a 起）
`fastFlowAnomalyDetector`	Detect anomalies using FastFlow network （自 R2023a 起）
`fcddAnomalyDetector`	Detect anomalies using fully convolutional data description (FCDD) network for anomaly detection （自 R2022b 起）
`maskrcnn`	Detect objects using Mask R-CNN instance segmentation （自 R2021b 起）

文本检测和识别

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

主题

目标检测

Getting Started with Object Detection Using Deep Learning (Computer Vision Toolbox)
Perform object detection and instance segmentation 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.
使用 R-CNN 深度学习训练目标检测器
此示例说明如何使用深度学习和 R-CNN（区域卷积神经网络）训练目标检测器。
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.

语义分割

Getting Started with Semantic Segmentation Using Deep Learning (Computer Vision Toolbox)
Segment objects by class using deep learning.
Train Simple Semantic Segmentation Network in Deep Network Designer
This example shows how to create and train a simple semantic segmentation network using Deep Network Designer.
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.
使用扩张卷积进行语义分割
使用扩张卷积训练语义分割网络。
使用深度学习对多光谱图像进行语义分割
此示例说明如何使用 U-Net 对包含七个通道的多光谱图像执行语义分割。
使用深度学习进行三维脑肿瘤分割
此示例说明如何基于三维医学图像执行脑肿瘤的语义分割。
定义使用 Tversky 损失的自定义像素分类层
此示例说明如何定义和创建使用 Tversky 损失的自定义像素分类层。
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).

视频分类

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.

精选示例

使用 Faster R-CNN 深度学习进行目标检测

此示例说明如何训练 Faster R-CNN（区域卷积神经网络）目标检测器。

打开实时脚本

使用 YOLO v2 深度学习进行目标检测

此示例说明如何训练 you only look once (YOLO) v2 目标检测器。

打开实时脚本

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.

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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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使用深度学习进行语义分割

此示例说明如何使用语义分割网络来分割图像。

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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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Detect Image Anomalies Using Explainable FCDD Network

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

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Classify Defects on Wafer Maps Using Deep Learning

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

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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.

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