객체 검출 및 인식 응용 사례
객체 검출 응용 사례를 위한 코드를 생성하고 임베디드 타깃에 배포합니다.
추천 예제
교통 표지판 검출 및 인식
딥러닝을 사용하는 교통 표지판 검출 및 인식 응용 사례를 위한 CUDA® MEX를 생성합니다.
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.
Lane Detection Optimized with GPU Coder
Develop a deep learning lane detection application that runs on NVIDIA® GPUs.
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.
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).
Generate Digit Images on NVIDIA GPU Using Variational Autoencoder
CUDA code generation for dlnetwork and
dlarray objects.
Code Generation for Object Detection by Using Single Shot Multibox Detector
Generate CUDA code for a singleshot multibox vehicle detector network.
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.
Code Generation for Lidar Object Detection Using PointPillars Deep Learning
Generate CUDA MEX for a PointPillars object detector.
Generate CUDA code for a Video Classification Network
Classify activities on Jetson™ Xavier using a network with convolutional and BiLSTM layers.
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 YOLO v2 딥러닝을 사용한 객체 검출 (Computer Vision Toolbox).
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.
YOLO v2를 사용한 객체 검출 코드 생성
YOLO(You Only Look Once) v2 객체 검출기에 대한 CUDA MEX를 생성합니다.
YOLO v3 딥러닝 신경망을 사용한 객체 검출 코드 생성
YOLO(You Only Look Once) v3 객체 검출기에 대한 CUDA MEX를 생성합니다.
