High-Precision Image Annotation for Autonomous Driving AI Model
Over a 6-month engagement, I contributed to building a robust training dataset for computer vision models in the autonomous driving domain. I annotated 18,000+ diverse urban and highway images, focusing on accurate detection and segmentation of dynamic objects in complex real-world scenarios (e.g., heavy traffic, varying weather, night/low-light conditions, and partial occlusions). Core tasks included: Precise bounding boxes for 15+ classes (vehicles, pedestrians, cyclists, traffic signs/lights, road debris, animals) Pixel-level semantic segmentation for road infrastructure (drivable surface, lanes, sidewalks, curbs) Detailed attribute labeling (e.g., vehicle orientation/pose, occlusion percentage, lighting/weather type, object behavior/intent) Strict adherence to client-specific guidelines, including edge-case handling and consistency checks I participated in regular quality audits, achieving >98% agreement on inter-annotator and gold-standard reviews. This work directly supported model training iterations, with client-reported improvements in downstream metrics (e.g., +12% mAP in object detection benchmarks).