Line and Polyline Annotation for Lane Detection

In the pursuit of safe, context-aware driving autonomy, understanding the structure of the road is as vital as detecting dynamic agents. Autonomous systems need to recognize not just vehicles and pedestrians—but also the boundaries that define safe paths: lane markings, road edges, dividers, and curbs.

This requires a specialized form of annotation: line and polyline annotation, where the task is not to label objects, but to trace continuous visual structures across a driving scene. Whether for lane-keeping assistance, full-stack autonomy, or HD map creation, these annotations form the skeleton of road geometry that machine learning models rely on.

In this blog, we explain what line and polyline annotation is, how it powers real-world lane detection systems, what challenges it introduces, and how FlexiBench supports the scale, accuracy, and governance that enterprise teams require to deploy these systems in production.

What Is Line and Polyline Annotation?

Line annotation refers to the labeling of straight or curved paths that follow visual cues in an image or video—such as road markings, sidewalks, or track boundaries. A polyline is a sequence of connected line segments, often used to represent curved or multi-directional shapes with more precision.

Unlike bounding boxes or polygons, which enclose an area, polylines represent paths. In the context of autonomous vehicles, they are typically used to label:

• Lane markers (dashed, solid, yellow, white)
• Road edges
• Divider lines
• Stop lines and crosswalks
• Curbs or barriers
  
  

Each polyline is defined by a set of ordered (x, y) points in image or world space, sometimes with associated metadata like lane type, color, or continuity.

When mapped over time or fused across modalities (e.g., camera + LiDAR), polylines become the reference framework for localization, navigation, and decision-making systems in autonomous vehicles.

Why Lane-Level Labeling Is Crucial for ADAS and Autonomy

In semi-autonomous and fully autonomous systems, the vehicle’s understanding of its position within a lane—and its prediction of where that lane leads—is critical for maintaining safety, avoiding edge cases, and complying with traffic regulations.

Lane detection models trained on polyline annotations enable features such as:

• Lane keeping assist (LKA) and lane departure warning (LDW) in ADAS
• Path prediction and trajectory planning in self-driving stacks
• HD map alignment and road topology reconstruction
• Safe overtaking and lane change maneuvers
• Understanding of non-standard road markings, including construction zones or temporary lane shifts
  
  

These systems depend on accurate, granular data that captures not just road geometry but also visual features under varying lighting, weather, and urban conditions.

Without reliable line annotation, these models suffer from spatial drift, misalignment with road edges, or poor generalization across road types.

Challenges in Annotating Lines and Polylines

Despite their visual simplicity, polylines are among the most nuanced annotation types. Their correctness hinges on both spatial precision and semantic consistency.

Common challenges include:

• Labeling in perspective: Road lanes appear curved and converged in forward-facing camera views. Annotators must place points based on visual projection, not real-world geometry.
• Occlusion and obstruction: Lanes may be partially obscured by vehicles, pedestrians, or weather conditions, requiring annotators to infer continuity.
• Multi-lane complexity: Urban scenes often contain a mix of lane types, intersections, bike lanes, and curbs—each with unique semantics.
• Temporal drift in video: In frame-by-frame annotation, even minor misalignments can cause polyline “wobble” across sequences, disrupting consistency.
• Manual fatigue: Tracing long or highly curved lines across multiple frames without assistance tools leads to annotator fatigue and reduced precision.
  
  

Addressing these challenges requires structured QA pipelines, robust tool support, and model-assisted labeling workflows.

Best Practices for Scalable Lane Annotation

To produce lane annotations that power production-grade models, enterprise teams should implement the following best practices:

1. Define a polyline schema: Every annotation should follow a strict schema defining lane type (solid/dashed), color, continuity, and directional class (left, right, center). This enables semantic richness beyond just visual tracing.
2. Use annotation tools with curve fitting and point interpolation: Advanced tools support Bézier curves or point auto-completion that maintain curvature continuity, especially critical in highways or intersections.
3. Leverage multi-view context: Annotators benefit from camera overlays, LiDAR projections, or map-based anchors to resolve ambiguities in line placement.
4. Implement reviewer scoring and agreement checks: QA reviewers should assess spatial consistency, semantic accuracy, and inter-annotator agreement to catch early-stage drift or labeling inconsistency.
5. Track annotation lineage and metadata: Every polyline should carry metadata—annotator ID, timestamp, instruction version, and review outcome—to ensure full traceability for downstream debugging or compliance audits.
   
   

How FlexiBench Supports Line and Polyline Annotation Workflows

FlexiBench enables enterprise AI teams to govern and scale polyline annotation workflows across internal reviewers, external vendors, and model-assisted pipelines—without platform lock-in or quality trade-offs.

Our infrastructure includes:

• Routing logic for complex road scenes, assigning tasks based on annotator experience, modality (video vs image), or geo-region
• Integration with high-precision annotation tools that support curve fitting, point snapping, and lane-type metadata
• Model-in-the-loop pipelines, where pre-trained detectors propose lane masks that annotators refine for higher throughput
• Temporal QA systems, where reviewers assess consistency across frames and scenes
  
  
• Versioned exports and audit logs, ensuring full traceability of every polyline decision, reviewer correction, and dataset iteration
• Performance dashboards, measuring per-lane annotation accuracy, reviewer agreement, and throughput cost per mile or per segment
  
  

With FlexiBench, line annotation moves from manual overhead to infrastructure-aligned precision labeling—designed for the demands of real-world autonomy.

Conclusion: Polylines Power the Driving Path

In autonomous systems, road understanding isn’t a nice-to-have—it’s existential. Lanes define where you can go, when you can turn, and how to share space with others. Annotating those lanes with precision, speed, and consistency isn’t just about data. It’s about trust.

Polylines may look simple. But they define how AI navigates the world.

At FlexiBench, we help you draw those lines—accurately, scalably, and with the infrastructure to support real-world performance.

References
KITTI Dataset, “Benchmarking Lane and Road Marking Annotation,” 2023
Waymo Open Dataset, “Lane Topology and Road Markings for AV Perception,” 2024
MIT CSAIL, “Curve-Aware Annotation in Urban Scenes,” 2023
NVIDIA DRIVE, “Model Training Using Polyline Road Features,” 2024
FlexiBench Technical Overview, 2024

‍