Edge AI Vision Kit powered by AIHub-Z5
Cloud-only video analytics can be too slow, too expensive, or too exposed when sites need local inference, weak-network resilience, and immediate event response. ZedIoT combines AIHub-Z5 edge AI hardware, cameras or RTSP streams, computer-vision models, local inference, alert rules, dashboards, and platform APIs.
Best fit for teams that need computer vision at the site
Industrial vision teams, warehouse and logistics operators, retail operations, equipment OEMs, security integrators, and system integrators
- Customer problem
- Cloud-only video analytics can be too slow, too expensive, or too exposed when sites need local inference, weak-network resilience, and immediate event response.
- ZedIoT delivery
- ZedIoT combines AIHub-Z5 edge AI hardware, cameras or RTSP streams, computer-vision models, local inference, alert rules, dashboards, and platform APIs.
- Expected outcome
- The result is an on-site computer-vision system that detects events locally and sends only actionable evidence to operations teams or business systems.
Run vision inference near cameras and equipment
AIHub-Z5 turns camera streams, models, and site events into local decisions before data leaves the location.
- RK3588 edge compute
- 6TOPS NPU
- RTSP or USB camera input
- Local inference and event filtering
How camera streams become actionable edge AI events
The kit keeps inference close to the scene, filters raw detections into useful events, and connects those events to dashboards, alerts, APIs, or business workflows.
Cameras and equipment
The kit starts from the real scene: camera position, lighting, viewing angle, object size, and operating rules.
AIHub-Z5 edge box
AIHub-Z5 handles local video inference, peripheral access, HDMI, Ethernet, Wi-Fi, and on-site application runtime.
Vision model and runtime
Models are converted, deployed, benchmarked, and tuned for the target camera stream and latency requirement.
Operations workflow
Events become alarms, dashboards, reports, device commands, tickets, or API records instead of staying as raw video.
What the solution includes
The scope is shaped around the scenario, existing devices, required integrations, operating team, and rollout stage.
Camera and stream integration
Connect IP cameras, RTSP streams, USB cameras, or equipment-side visual inputs based on the site layout.
Model deployment on AIHub-Z5
Deploy YOLO, OCR, object recognition, defect detection, safety detection, or custom computer-vision models at the edge.
Local event filtering
Turn raw detections into useful events with thresholds, zones, time windows, duplicate suppression, and confidence rules.
Dashboard and API handoff
Send event evidence, alerts, snapshots, counts, or status to ZedIoT dashboards or customer platforms through APIs.
AIHub-Z5 is the compute core of the kit
The kit packages hardware, model deployment, camera integration, event rules, dashboard workflows, and API handoff around the on-site vision task.
- Confirm the target scene, camera position, lighting, object size, and success metric before selecting model and hardware settings.
- Validate latency, accuracy, false positives, and model confidence at the pilot site before scaling to more locations.
- Define what data stays local, what evidence is uploaded, and how long video or snapshots should be retained.
- Plan OTA, model versioning, remote diagnostics, and rollback if the kit will be deployed across many sites.
Questions to answer before a solution pilot
Can the Edge AI Vision Kit run without constant cloud connectivity?
Yes. The vision inference and event filtering can run locally on AIHub-Z5. Cloud or platform connectivity is still useful for dashboards, alerts, reports, OTA, and fleet operations.
What camera types can be connected?
The kit can be planned around IP cameras, RTSP streams, USB cameras, or project-specific visual inputs. The final choice depends on resolution, frame rate, lighting, distance, and installation limits.
Can we use our own model?
Yes. Existing YOLO, OCR, defect detection, or custom computer-vision models can be evaluated, converted, benchmarked, and integrated if the model format and performance target are clear.
Is AIHub-Z5 enough for multiple camera streams?
It depends on model size, resolution, frame rate, detection frequency, and post-processing logic. A pilot should benchmark representative streams before committing to deployment density.
Can it integrate with our existing platform?
Yes. Events, snapshots, counts, alarms, and status can be pushed to customer platforms through APIs, MQTT, HTTP, webhooks, or custom integration middleware.
Talk to an AI-IoT engineering team
Share your product idea, current hardware, target workflow, or integration challenge. We will help you evaluate the fastest path to a working prototype and production-ready system.
- AI + IoT product architecture review
- Hardware, firmware, cloud, and application integration
- Prototype planning and production support