Object Detection and Recognition Applications

Generate code for deep learning networks that perform object detection

Generate code for object detection applications and deploy on embedded targets.

Featured Examples

Traffic Sign Detection and Recognition

Generate CUDA^® MEX for a traffic sign detection and recognition application that uses deep learning.

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Code Generation for Object Detection Using YOLO v4 Deep Learning

Generate plain CUDA code without dependencies on deep learning libraries for YOLO v4 object detector.

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Lane Detection Optimized with GPU Coder

Develop a deep learning lane detection application that runs on NVIDIA^® GPUs.

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Deep Learning Prediction with NVIDIA TensorRT Library

Generate a CUDA MEX file that performs 32-bit, 16-bit floating point, and 8-bit integer prediction using TensorRT.

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Code Generation for Detect Defects on Printed Circuit Boards Using YOLOX Network

Generate code for a You Only Look Once X (YOLOX) object detector that can detect, localize, and classify defects in printed circuit boards (PCBs).

Since R2023b
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Generate Digit Images on NVIDIA GPU Using Variational Autoencoder

CUDA code generation for dlnetwork and dlarray objects.

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Code Generation for Object Detection by Using Single Shot Multibox Detector

Generate CUDA code for a singleshot multibox vehicle detector network.

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Code Generation for Lidar Point Cloud Segmentation Network

Generate CUDA MEX for a network that can segment organized lidar point clouds belonging to three classes.

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Code Generation for Lidar Object Detection Using PointPillars Deep Learning

Generate CUDA MEX for a PointPillars object detector.

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Code Generation for a Video Classification Network

Classify activities on Jetson™ Xavier using a network with convolutional and BiLSTM layers.

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Code Generation for a Deep Learning Simulink Model That Performs Lane and Vehicle Detection

Develop a CUDA® application from a Simulink® model that performs lane and vehicle detection using convolutional neural networks (CNN). This example takes the frames of a traffic video as an input, outputs two lane boundaries that correspond to the left and right lanes of the ego vehicle, and detects vehicles in the frame. This example uses the pretrained lane detection network from the Lane Detection Optimized with GPU Coder example of the GPU Coder™ product. For more information, see Lane Detection Optimized with GPU Coder. This example also uses the pretrained vehicle detection network from the Object Detection Using YOLO v2 Deep Learning example of the Computer Vision Toolbox™. For more information, see Object Detection Using YOLO v2 Deep Learning (Computer Vision Toolbox).

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Deploy and Classify Webcam Images on NVIDIA Jetson Platform from Simulink

Deploy a Simulink® model on the NVIDIA® Jetson™ board for classifying webcam images. This example classifies images from a webcam in real-time by using the pretrained deep convolutional neural network, ResNet-50. The Simulink model in the example uses the camera and display blocks from the MATLAB® Coder™ Support Package for NVIDIA Jetson and NVIDIA DRIVE™ Platforms to capture the live video stream from a webcam and display the prediction results on a monitor connected to the Jetson platform.

Code Generation for Object Detection by Using YOLO v2

Generate CUDA MEX for a you only look once (YOLO) v2 object detector.

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Code Generation for Object Detection Using YOLO v3 Deep Learning Network

Generate CUDA MEX for a you only look once (YOLO) v3 object detector.

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