Autonomous Vehicle Object Detection & Annotation Project
Annotated over 45,000 street-level images for a computer vision model used in autonomous driving systems. Responsibilities included: Drawing precise bounding boxes for vehicles, pedestrians, cyclists, traffic signs, and road obstacles Performing pixel-level semantic segmentation for road lanes and sidewalks Classifying objects by category and sub-category (e.g., emergency vehicle vs. private vehicle) Conducting quality assurance checks to maintain ≥98% annotation accuracy Maintained strict adherence to annotation guidelines, performed inter-annotator agreement reviews, and corrected edge-case inconsistencies such as occlusions and motion blur scenarios. Improved model training precision by ensuring high-quality, bias-reduced labeled datasets.