Lidar Code-Along
Load Point Cloud Data
Load point cloud data as a data store using the pcread function and load the bounding box labels using the boxLabelDatastore function.Preprocess Data
Split the data into training and testing sets. Synthetically increase the size of the training data set by randomly adding a fixed number of car and truck class objects to every point cloud and by using augmentation techniques such as flipping, scaling, rotation, and translation. To learn more about typical data augmentation techniques for 3-D object detection workflows with lidar data, see Data Augmentations for Lidar Object Detection Using Deep Learning.Define Deep Neural Network
Define network parameters, such as anchor boxes and pillars, for the PointPillars network. Then, define the PointPillars detection network using the pointPillarObjectDetector function.Train Network
To train a network, you must specify training options. Because training a network can be time consuming, a pretrained PointPillars model is used for this workflow.Object Detection
Test the PointPillars network on a test dataset and display the detected output point cloud with bounding boxes.- Category:
- AI Application,
- Code-Along,
- Deep Learning
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