A Practical Guide to Labels Behind Computer Vision Models

In defense and security applications, where precision, reliability, and situational awareness are critical, the performance of computer vision models depends in 80% on the inputted labeled data.

Annotation is the process of adding structured information to raw image or video data so that AI systems can learn to interpret the visual world. It enables models to recognize threats, classify targets, estimate movement, and understand complex scenes with real-time accuracy.

Whether you’re developing autonomous surveillance systems, battlefield perception modules, or tactical vision-enhanced robotics, selecting the right type of annotation is foundational. Let’s explore the most common annotation types used in modern computer vision, and how they apply to real-world security and defense scenarios.

1. Class Labels: Identifying What’s Present

Class labels assign a category to an image or object—for example, vehicle, person, or drone. These labels form the basis for training classification models and object detectors.

Example of use cases:

• Object classification in aerial imagery
• Object filtering
• Scene recognition in reconnaissance

Please note: Class labels alone do not localize objects within the scene.

2. Instance Labels: Differentiating Between Multiple Objects

Instance-level annotations distinguish between individual objects of the same class. For example, labeling three separate vehicles in a convoy allows a model to track each one independently.

Example of use cases:

• Multi-object tracking
• Crowd monitoring
• Vehicle differentiation

Why it matters: In dynamic environments, treating each object as a unique instance supports better tracking and behavior prediction.

3. 2D Bounding Boxes: Fast, Efficient Object Localization

2D bounding boxes provide rectangular annotations around objects in the image plane. They’re one of the most widely used and efficient forms of annotation.

Example of use cases:

• Perimeter monitoring
• Drone-based object detection
• Real-time person or vehicle tracking

In many cases 2D bounding boxes involve a trade-off: While fast to annotate and process, 2D boxes may include background clutter and lack precision around irregular shapes.

4. 3D Bounding Boxes: Adding Depth and Orientation

3D bounding boxes extend 2D boxes into three-dimensional space, capturing not just the position but also the volume and orientation of an object.

Example of use cases:

• Ground vehicle and UAV detection using multi-view sensors
• Path prediction for autonomous patrol units
• Object classification with spatial awareness

Challenge: Requires calibrated sensors or synthetic environments to generate accurate annotations. Impossible to annotate manually.

5. Depth Maps: Measuring Distance from the Sensor

Depth annotations provide per-pixel distance values between the sensor and surfaces in the scene. This information adds a critical third dimension to visual data.

Example of use cases:

• Obstacle avoidance for unmanned systems
• Terrain analysis
• Tactical path planning

Data sources: Common technologies used to generate depth maps are for example, Time-of-Flight and Light Detection and Ranging (LiDAR).

6. Surface Normals: Understanding Object Geometry

Surface normal annotations describe the 3D orientation of surfaces at pixel level. Essentially, they tell the system which direction a surface is facing.

Example of use cases:

• Grasp planning in robotics
• Scene understanding for indoor navigation
• Material and shape analysis in reconnaissance

Value-added of the label: Normals complement depth information, enabling more accurate interaction with physical environments.

7. Keypoints: Tracking Structure, Pose, and Movement

Keypoints mark specific, meaningful locations on an object—like a person’s joints or the corners of a drone.

1. 2D keypoints reside in the image space
2. 3D keypoints include spatial depth for full pose estimation

Example of use cases:

• Human pose estimation in surveillance
• UAV or robot pose tracking
• Action recognition in security video analysis

Strategic advantage: Keypoints offer a lightweight yet highly descriptive representation of structure and movement.

8. Color Labels: Appearance-Level Semantics

Color and material annotations add appearance-related information, helping the model understand surface properties or visual contrast patterns.

Example of use cases:

• Camouflage detection
• Synthetic data rendering
• Scene segmentation by material type (e.g., concrete vs. vegetation)

Please note: Consistent, clear, and well-defined color annotation protocols, combined with careful quality control and awareness of potential biases, will help ensure that your models learn meaningful visual features and generalize well to real-world data

Matching Annotation Types to Operational Needs

Not all projects require every type of annotation. For example:

• A fixed surveillance system may only rely on class labels and 2D bounding boxes.
• An autonomous UGV navigating hostile terrain may need depth maps, surface normals, and 3D boxes.
• A drone-based reconnaissance platform benefits from 3D keypoints for identifying and tracking moving targets.

Choosing the right annotation mix is a strategic decision that directly affects model performance, operational efficiency, and deployment success.

Final Thoughts

In high-stakes environments, computer vision models must do more than just see—they must understand. That understanding begins with the right annotations. In defense and security, where access to diverse, annotated data can be limited or classified, synthetic data is a key enabler. Synthetic environments can generate rich, multi-modal annotations—including depth, normals, and 3D pose—at scale and with full control over conditions (lighting, weather, occlusion, etc.). Leveraging synthetic data ensures consistency, reduces annotation effort, edge case coverage and allows rapid iteration—all without compromising security or compliance.