Data Labeling — How Auto-Driving using Machine Learning?
Training Self-Driving Car Using Deep Learning
Supervised Deep Learning Needs Labeled Data at Scale
The mainstream algorithm model of autonomous driving is mainly based on supervised deep learning. It is an algorithm model that derives the functional relationship between known variables and dependent variables. A large amount of structured labeled data is required to train and tune the model.
Labeling Types in Auto-Driving
Common annotation types in auto-driving usually include 2D bounding box, 3D bounding box, polygon, image segmentation, video annotation, 3D point cloud annotation, etc., among which image semantic segmentation is widely used.
Conceptually, image semantic segmentation is an important annotation type in computer vision. It includes image classification, target detection, image segmentation, and main segmentation at the pixel level.
Image segmentation
The result of semantic segmentation is to transform the image into several color blocks, and each color block represents one part of the image.
These annotated segmentation images can be used to train the algorithm. During the driving process of the self-driving car, the images detected by the onboard camera or radar are input into the neural network system, and the well-trained algorithm model can automatically segment and classify the images, so as to avoid obstacles such as pedestrians and vehicles on the road.
Currently, in the field of semantic segmentation of auto-driving images, the commonly used annotation objects mainly are into the following categories
01. Accessible Road areas
The driving zone usually refers to the area where cars can drive.
02. Barrier
Located on both sides of the road, mainly including railings, roadblocks, and other barriers.
03. Traffic sign(the vertical part)
Only the vertical part should be labeled, such as street signs, traffic lights, etc.
04. Roadside buildings
Buildings on both sides of the road, including high-rise buildings and low-level buildings, and other man-made objects.
End
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