Image Data Collection

Definition

Image data collection is the process of gathering visual datasets for training computer vision systems. Sources include cameras, drones, satellites, and public datasets.

Purpose

The purpose is to ensure models have diverse examples for learning visual patterns across environments and use cases.

Importance

• Critical for computer vision model accuracy.
• Must include varied lighting, angles, and demographics to avoid bias.
• Raises privacy and consent issues when collecting human images.
• High storage and management demands.

How It Works

1. Define project goals and data needs.
2. Collect images via sensors, APIs, or repositories.
3. Organize and label metadata for traceability.
4. Store securely for annotation and training.
5. Continuously update datasets for relevance.

Examples (Real World)

• ImageNet: large-scale visual dataset for AI.
• COCO Dataset: collected and annotated images for research.
• Google Street View: camera-collected images for mapping and vision tasks.

References / Further Reading

• ImageNet Project — Princeton & Stanford.
• COCO Dataset — cocodataset.org.
• ISO/IEC TR 20547-5: Big Data Reference Architecture.