Autonomous Vehicle Perception System
Large-scale annotation project for training autonomous driving perception models, including pedestrian detection, vehicle tracking, lane marking, and traffic sign recognition across diverse driving conditions.
The Challenge
The client needed to annotate 2 million+ images and 10,000+ hours of video footage captured from multiple camera angles in various weather conditions, lighting scenarios, and geographic locations. Annotations required pixel-perfect accuracy for safety-critical applications with complex occlusion handling.
Our Solution
Deployed a team of 150 specialist annotators working with custom annotation tools. Implemented multi-stage quality control with automated validation, peer review, and expert verification. Created detailed annotation guidelines covering 85 object classes with specific handling instructions for edge cases.
Project Specifications
Data Volume
2.5M images, 10,000+ video hours
Team Size
150 specialists
Duration
6 months
Accuracy
99.3%
Annotation Types
Tools & Technologies
Deliverables
Sample Annotations
Pedestrian Detection
Precise bounding boxes and keypoint annotations for pedestrians in various poses and occlusion scenarios
Lane Marking Segmentation
Pixel-perfect polyline annotations for solid, dashed, and complex lane markings under different road conditions
Traffic Sign Recognition
Multi-class classification and localization of 85+ traffic sign types with attribute tagging
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