Object Detection
Find & classify objects at scale
Object detection is Nexma's perception capability — finding and classifying objects in imagery and video at scale, across land, sea, and air. Vehicles, vessels, aircraft, infrastructure, terrain: detected, classified, tracked across frames, and written straight into the world model as typed entities. Imagery and video arrive faster than any team can watch; the InsightEngine turns that flood into something you can query and act on.
It is the same operating system — DataStore, Ontology, Jax, and the engines — with the perception layer doing the watching.
What you can do
- Detect and classify at scale. Production-grade computer vision finds objects across overhead imagery and full-motion video, with a confidence score on every hit.
- Track over time. Object identity is held across frames and passes, so a vessel is followed through time rather than re-detected as a stranger.
- Fuse many sensors. Satellite, drone video, and ground sensors merge into one deduplicated, georeferenced picture — no single-source blind spots.
- Detect change. Automated before-and-after analysis of any area flags new construction, movement, and encroachment without frame-by-frame review.
- Index and query. Every detection is geolocated and indexed in the DataStore, queryable by area, time, class, or confidence, with a full audit trail.
Core concepts
A bounding box in a viewer is not actionable. The point of detection in Nexma is to make perception part of the world model — so a detection is a typed entity that can trigger a workflow, not a pixel rectangle that sits in a tab.
- Detections become entities. Results land in the DataStore typed by the active Ontology, carrying model, confidence, and source. A detection can launch an alert, a task, or a dispatch.
- Tracking gives the picture memory. Holding identity across frames means the operational picture knows what has appeared, moved, or changed since the last pass — not just what is in the current frame.
- Fusion gives one truth. Hits from every sensor land on the same map, deduplicated and georeferenced, so teams reason from one shared picture instead of a stack of disconnected viewers.
A detection that lives in the world model can trigger an agentic workflow automatically. A detection in a standalone viewer cannot. That difference is the whole capability.
How it works
Point the perception layer at any overhead or motion feed. It detects, tracks, fuses, and grounds — turning raw pixels into entities the rest of the platform understands.
1Feed: full-motion video over an area of interest
2 1. Detect classify every object in frame, with confidence
3 2. Track lock a track onto each one; hold identity across frames
4 3. Fuse merge with EO, SAR, and ground-sensor hits → one picture
5 4. Ground write typed entities to the DataStore (model + confidence + source)
6 5. Act a detection can trigger an AgentEngine workflowModels watch continuously, so events surface automatically instead of waiting in an analyst's backlog.
Perception across modalities
| Stage | What it covers |
|---|---|
| Perceive | Detection across EO, SAR, and full-motion video; change detection; multi-modal input |
| Track and fuse | Temporal tracking across frames and passes; deduplicated, georeferenced fusion; confidence and provenance per hit |
| Ground and act | Typed entities in the DataStore; workflow triggers; full history retained for replay |
Example
A port authority needs continuous awareness of vessel activity in a restricted anchorage.
- Point the perception layer at the live satellite and coastal-camera feeds over the anchorage.
- Detection classifies every vessel in frame and locks a track onto each; AIS hits are fused so dark vessels stand out from broadcasting ones.
- Each detection is written to the DataStore as a typed entity with model, confidence, and source.
- A vessel entering the restricted zone without an AIS broadcast crosses a threshold, and the detection triggers an AgentEngine workflow that alerts the watch.
- Later, the team queries every detection in the zone over the past week by class and confidence to build a pattern-of-life view.
Swap the Ontology and the same detect-track-fuse-ground loop serves construction-progress monitoring, wildlife survey, or asset inventory. The perception is generic; the meaning comes from the world model.
Where to go next
- Nexma InsightEngine — the perception layer that powers detection.
- Satellite Imagery — task and analyze the overhead imagery detection runs on.
- Nexma AgentEngine — turn a detection into an automated response.
- DataBase — track detected objects through time at scale.
- Investigation — trace detected objects into an entity network.