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 in diverse urban and highway environments. My responsibilities included: Bounding Box & Polygon Annotation: Precisely labeling vehicles, pedestrians, cyclists, traffic signs, and road infrastructure. Segmentation & Tracking: Creating pixel‑level masks for lane markings and tracking moving objects across video sequences. Quality Assurance: Implementing a double‑review process with inter‑annotator agreement checks to ensure >95% accuracy. Scalability: Coordinating with a distributed team to meet weekly annotation quotas while maintaining consistency across classes. This project directly supported model training for real‑time object detection and scene understanding, improving safety and reliability in autonomous navigation.