Road Lane Instance Segmentation

Classes:

The dataset categorizes road lane markings into the following classes:

• Divider Line: Solid lines used to separate traffic moving in opposite directions, typically found in the center of the road.
• Dotted Line: Broken lines used for lane division or indicating areas where lane changes are permitted.
• Double Line: A pair of solid lines, often indicating areas where overtaking is prohibited.
• Random Line: Miscellaneous markings such as temporary construction lines, which may not follow standard lane marking patterns.
• Road Sign Line: Specialized markings indicating directions, such as turn arrows or crosswalks.
• Solid Line: Continuous lines used to define the edge of a lane or boundary where lane changes are not allowed.

Data Annotations:

• Instance Segmentation: Each lane marking is segmented with a unique instance mask, allowing algorithms to distinguish between individual lanes and classify them accordingly.
• Bounding Boxes: In addition to instance segmentation masks, the dataset also provides bounding box annotations for lane markings.
• Lane Curvature and Width: Some annotations include additional metadata relate to the curvature and width of lanes, useful for training models on highways, city streets, and rural roads.

Applications:

This dataset is suitable for a wide range of computer vision tasks, including:

• Autonomous Driving: The dataset is ideal for developing systems capable of lane detection and navigation in autonomous vehicles, enabling safe lane-keeping and transition maneuvers.
• Advanced Driver Assistance Systems (ADAS): Useful for real-time lane detection and warning systems in smart vehicles.
• Road Condition Monitoring: Can be use by transportation authorities to identify worn-out or missing lane markings that need maintenance.
• Traffic Management Systems: Enables smart city infrastructure by integrating road lane data for efficient traffic flow management.

Dataset Features:

• Diverse Road Conditions: The images cover a wide variety of road conditions, including urban, suburban, and highway environments, with varying lighting conditions (day/night) and weather situations (rain, snow, fog).
• Resolution and Quality: All images are high-resolution, ensuring clear visualization of lane details, even in complex or challenging scenes.
• Scalable and Customizable: The dataset is expandable with additional road classes and can be customize according to specific user requirements or research goals.
• Platform Compatibility: The dataset can be seamlessly integrate into machine learning platforms like TensorFlow, PyTorch, and others, with annotations provide in commonly use formats such as COCO and Pascal VOC.