Standard DatasetsLane Line Segmentation Dataset
Welcome to the fascinating world of autonomous driving, powered by our rich and diverse datasets. These datasets, meticulously curated and annotated, are the lifeblood of the self-driving car industry, fueling advancements across various domains.
The "Lane Line Segmentation Dataset" is designed to accelerate advancements in autonomous driving technologies, specifically focusing on lane detection and segmentation. It includes a vast array of images from driving recorders, segmented into 35 distinct categories to cover a comprehensive range of road markings such as various solid and dashed lines in white and yellow. This dataset aims to refine the precision of AI in identifying lane boundaries, crucial for the safe navigation of autonomous vehicles.
Sample
Specification
Dataset ID
MD-Auto-002
Dataset Name
Lane Line Segmentation Dataset
Data Type
ImageVolume
About 135.3k
Data Collection
Driving recorder image
Annotation
Semantic Segmentation,Binary Segmentation
Annotation Notes
The various elements of road surface are divided into 35 categories, including non-ground, white solid line, yellow solid line, white double solid line, yellow double solid line, white solid line and dashed line, yellow solid line and dashed line, white dashed line, yellow dashed line, white dashed line, white double dashed line, yellow double dashed line, white double solid line, yellow double solid line and so on.
Application Scenarios
Autonomous Driving
Data Collections
With approximately 135.3k annotated images, this dataset offers detailed visibility into lane markings, supporting the development of AI systems for autonomous vehicles. By providing diverse road marking examples, it helps in training AI models to accurately interpret and navigate lane lines, enhancing road safety for autonomous driving.
Quality Assurance
Relevant Open Datasets
To supplement our Face Parsing Dataset, users can explore these open datasets for additional resources:
Cityscapes Dataset [Learn more]
Focuses on semantic understanding of urban street scenes, featuring semantic, instance-wise, and dense pixel annotations for various classes. It includes 5,000 finely annotated images and 20,000 coarsely annotated images.
Waymo Open Dataset [Learn more]
Offers a high-quality multimodal sensor dataset for autonomous driving extracted from Waymo self-driving vehicles, covering a wide variety of environments and conditions.
nuScenes Dataset [Learn more]
A comprehensive dataset for autonomous driving that enables researchers to study urban driving situations using the full sensor suite of a real self-driving car. The dataset features camera images, lidar sweeps, and detailed map information.
A2D2 Dataset [Learn more]
The Audi Autonomous Driving Dataset (A2D2) offers a large volume of data with various annotations, including semantic segmentation and 3D bounding boxes.
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