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Road Scene Semantic Segmentation Dataset

The "Road Scene Semantic Segmentation Dataset" is specifically designed for autonomous driving applications, featuring a collection of internet-collected images with a standard resolution of 1920 x 1080 pixels. This dataset is focused on semantic segmentation, aiming to accurately segment various elements of road scenes such as the sky, buildings, lane lines, pedestrians, and more, to support the development of advanced driver-assistance systems (ADAS) and autonomous vehicle technologies.

Sample

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Specification

Dataset ID
MD-Image-067
Dataset Name
Road Scene Semantic Segmentation Dataset
Data Type
data-typeImage
Volume
About 2k
Data Collection
Internet collected images. Resolution is 1920 x 1080.
Annotation
Semantic Segmentation
Annotation Notes
Segment the instances of road scenes, including sky, buildings, lane lines, person, etc.
Application Scenarios
Auto Driving

Data Collections

This dataset includes approximately 2k images, each segmented to identify and classify key components of road scenes crucial for autonomous driving. The detailed segmentation of elements like lane lines, traffic signs, pedestrians, and vehicles provides essential data for training and validating algorithms responsible for vehicle perception, navigation, and decision-making, ultimately contributing to the safety and efficiency of autonomous driving systems.

Quality Assurance

Quality Assurance

Related products

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