The Innomium Vision program explores how compact detection systems can move from a promising benchmark to a dependable operational signal. We work across scene-specific evaluation, data design, model adaptation, ONNX packaging, edge inference, and alert behavior—because the detector is only one part of the decision system.
Program operating system
Hypothesis · Evaluation · Artifact · Decision
Sentinel, Vantage, and Ember
Edge, CPU, and browser-oriented evaluation
Validate on the cameras and conditions that matter
Program thesis
Accuracy on a convenient dataset is not enough. Camera position, weather, lighting, motion blur, object scale, occlusion, hardware limits, network conditions, and the cost of a false alert all reshape the real problem. The program treats those factors as first-class research variables, then turns the strongest findings into inspectable releases and deployment guidance.
QUESTION 01
We examine how scale, occlusion, illumination, weather, viewpoint, compression, and background texture alter detector behavior—and where tuning helps or merely hides a weak data foundation.
QUESTION 02
We study the trade between local inference, central processing, browser execution, and hybrid architectures across latency, privacy, bandwidth, updateability, observability, and hardware cost.
QUESTION 03
A box is not an operational outcome. We evaluate thresholds, temporal confirmation, zone logic, hard-negative handling, escalation, and human review as part of the complete alert pathway.
Connected research workstreams
The model is never treated in isolation. Evaluation, data, runtime, integration, and operational ownership are part of the same research question.
Define the acceptance problem before optimizing the model. We shape representative splits, hard-negative sets, class definitions, annotation rules, scene cohorts, and metrics that reflect the consequences of misses and false positives.
Compare baseline detectors, tune for the target domain, test augmentation and post-processing, and reduce model size only where the evidence shows that operational quality can be retained.
Package models for practical runtimes, profile preprocessing and post-processing, measure latency and memory on representative hardware, and design a path for versioning and rollback.
Connect detections to the operating process. We define event semantics, confidence and temporal rules, review interfaces, observability, and feedback capture so model behavior can be governed after release.
Evidence-led method
Progress is measured by the quality of the evidence and the decision it enables—not by the number of experiments completed.
Inventory cameras, scenes, frame quality, available labels, hardware, network paths, privacy boundaries, and response workflows.
Evidence: Constraint map and evaluation design
Measure a simple, reproducible model first. Segment results by scene and failure mode instead of optimizing only a single aggregate score.
Evidence: Baseline report and error taxonomy
Improve data, model, preprocessing, post-processing, and temporal logic together while tracking the cost and value of each change.
Evidence: Ablations, model package, and runtime profile
Pilot on held-out scenes, test alert behavior and recovery, document limitations, and define the evidence required for a broader rollout.
Evidence: Pilot decision and productionization plan
Applied value
These are representative application patterns, not undisclosed client claims. Feasibility depends on the data, workflow, risk, and operating environment.
Evaluate fire, smoke, restricted-zone, or equipment-related signals where early attention may improve response. The model supports the workflow; it does not replace safety controls or accountable human decisions.
Detect relevant vehicle classes or movements as inputs to congestion, gate, dock, or exception workflows without assuming that generic road footage represents a specific site.
Explore privacy-conscious people-detection patterns for capacity, queue, or operational visibility, with explicit policies for retention, review, and acceptable use.
Add compact detection to a device or software product with a defined model-update path, runtime budget, telemetry design, and responsibility for failure handling.
Compact person-detection skill for dense scenes — public ONNX package with a live browser demo.
Vehicle-detection skill for roadways and logistics scenes — compact ONNX with a public demo.
Outdoor fire and smoke detection skill — public demo and edge-oriented ONNX packaging.
Research boundaries
Public artifacts accelerate technical diligence. They do not remove the obligation to validate on your data, infrastructure, risk model, and operating process.
Reported release figures describe specific Innomium protocols. They are not guarantees for a new camera, scene, geography, class definition, or operating threshold.
High-consequence workflows require redundancy, human oversight, escalation policy, and testing beyond the model. Computer vision should not be presented as the sole safety mechanism.
Data minimization, local processing, access control, retention, reviewability, and legal assessment belong in the architecture—not in a policy document added after deployment.
Research to delivery
Research can lead into a managed build, a dedicated specialist team, an Arena challenge, or a clear decision not to proceed.
Closed challenge families from our public Arena program. Browse live Arena listings for current rules and leaderboards.
Completed Apr 2026 · 96 participants
View Arena challengesCompleted Mar 2026 · 128 participants · integrated into Sentinel QA
View Arena challengesSentinel, Vantage, and Ember on Hugging Face.
Bring the scenes, hardware, workflow, and consequence of error. We will define the evaluation path, adaptation work, and operating evidence a responsible first phase should produce.
Built for accountable delivery
We begin with the operating constraint, agree on what success looks like, and build a delivery path your technical and business teams can review.
01
Scope, constraints, milestones, and decision owners before build work starts.
02
Evaluation plans, working artifacts, and reviewable technical decisions—not presentation-only progress.
03
Integration, observability, documentation, and an operating path for the teams who own the result.