Image Annotator for Autonomous Vehicle Dataset
Worked on a large-scale data labeling project for a leading autonomous vehicle company. The goal was to annotate visual data to train perception models for self-driving cars. Scope & Scale: Annotated over 50,000 images and 10, hours of video footage captured from urban and highway environments. Specific Tasks: Drew precise bounding boxes around vehicles, pedestrians, cyclists, and traffic lights Performed semantic segmentation to label road surfaces, lane markings, crosswalks, and sky Completed LiDAR annotation for 3D point cloud data to identify objects in spatial context Labeled traffic sign categories (stop signs, speed limits, yield signs, etc.) Quality Measures: Followed detailed annotation guidelines and style guides Maintained 95%+ accuracy through regular quality audits Participated in calibration exercises and feedback loops with quality assurance team Reviewed and corrected annotations based on client feedback