Autonomous Vehicle Object Detection Dataset Annotation
I contributed to a large-scale annotation project supporting the development of autonomous driving systems. The dataset consisted of over 250,000 images and video frames captured from urban, suburban, and highway environments. My primary tasks included: • Bounding Box & Polygon Annotation: Identifying and labeling vehicles, pedestrians, cyclists, traffic signs, and road infrastructure. • Segmentation & Tracking: Pixel-level segmentation of lane markings and continuous tracking of moving objects across video frames. • Quality Assurance: Implemented multi-stage review processes, including inter-annotator agreement checks and automated validation scripts, ensuring >98% accuracy across labeled datasets. • Scalability: Designed annotation guidelines and trained new team members, enabling consistent labeling across a distributed workforce. This project directly supported the training of object detection and tracking models used in real-time navigation and collision avoidance systems.