Computer Vision Data Annotation for Autonomous Driving Systems
Annotated and processed over 500,000+ images and video frames for autonomous driving AI models, focusing on high-precision object detection and scene understanding. Applied bounding boxes, polygons, and keypoint annotations to label vehicles, pedestrians, road signs, and lane markings. Performed multi-frame object tracking across complex traffic scenarios, ensuring temporal consistency in datasets. Maintained 98–99% annotation accuracy by strictly following labeling guidelines and conducting detailed quality assurance checks. Collaborated with AI engineers to refine annotation standards, contributing to a 15% improvement in model performance. Successfully handled large-scale datasets under tight deadlines, delivering 100% on-time project completion while reducing error rates by 20% through validation processes.