Bounding box annotation is a foundational technique in computer vision, involving the drawing of rectangles around objects of interest—people, vehicles, products—for model training. Expert annotation teams handle variations in scale, occlusion, and perspective across vast image sets. Scalable pipelines ensure consistency, with standardized labeling rules and quality assurance checks. These accurate annotations empower models to learn object localization, tracking, and detection with high confidence. Applied across autonomous driving, retail analytics, surveillance, and robotics, precise bounding boxes enable AI systems to delineate object boundaries, improve accuracy, and enhance real-time performance, accelerating deployment of intelligent visual solutions in real-world environments.