YOLO and JEPA
We use YOLO and JEPA for computer vision tasks. We select the specific model and adaptation method around the process and site data.
TECHNOLOGY / OUR DESIGN APPROACH
VIDEO INTO EVENTS.
EVENTS INTO ACTIONS.
A model is part of a system. Practical results also need suitable data, business rules, a review interface and clear responsibilities.
Busted is a computer vision integrator. We use YOLO, JEPA, local models and VLM APIs for selected tasks. This is our design approach; the specific architecture and integrations are defined in the project specification.
BUSTED / OUR STACK
WE CHOOSE MODELS
FOR YOUR TASK.
Local models and VLM APIs
We use local models and, for selected parts of a task, VLM API requests. Processing steps and the data sent are agreed during design.
01 / ARCHITECTURE
HOW THE SIGNAL TRAVELS.
- 01Video
A selected zone and a suitable camera angle.
- 02Analysis
Objects, actions and an agreed rule.
- 03Event
Time, area and context for review.
- 04Response
The responsible person checks the event and records the outcome.
From recognition to a rule
Detection finds objects; tracking connects them across frames; action analysis helps assess observable steps. A rule turns features into an event, such as a zone remaining empty beyond an allowed interval.
From event to decision
An event contains the area, time and context. It appears in a log and a notification. The assignee reviews the footage, records the outcome and chooses the next action. Channels and systems are agreed for each project.
02 / END-TO-END EXAMPLE
A CLEAR SIGNAL.
CONTEXT YOU CAN VERIFY.
EVENT / 10:42:18AWAITING REVIEW
Check head covering
- Where
- Production zone 2 entrance
- Rule
- Zone entry → visible PPE check
- Context
- Video clip and timestamp
- Assigned to
- Quality team
- Next action
- Review the clip and check compliance with the procedure
LOG → REVIEW → OUTCOME → REPORT
Illustrative interface layout with sample data. This is not footage of a working model or a deployment result.
03 / MODELS AND DATA
SELECT.
VALIDATE. ADAPT.
Defining the task
We identify what is visible and what counts as an event. We assemble examples of normal situations, deviations and acceptable exceptions.
Preparing data
Validation needs footage from different shifts, angles and conditions. We separate adaptation and validation data so the model is not evaluated only on familiar frames.
Choosing an implementation
Using an existing model, fine-tuning it and developing a new architecture involve different amounts of work. The choice depends on the task and data.
Testing the workflow
Recognition alone is not enough: we test the rule, recipient, usefulness of the context and whether staff can respond in time.
04 / PERFORMANCE
ONE ACCURACY FIGURE
IS NOT ENOUGH.
| Metric | How to measure it |
|---|---|
| Missed events | The share of actual events of the selected type that the system failed to flag, measured against independent annotations of test footage. |
| False alerts | The proportion of issued alerts that are false, and the number of false notifications per camera per shift. |
| Latency | Time from the agreed event point to the notification appearing for the assignee. |
| Stability | Processing availability, stream loss, load and performance as lighting, angle or product range changes. |
| Business metric | Waiting, downtime or selected losses before and after deployment, under comparable load over an agreed period. |
Thresholds, test duration and acceptable error rates are agreed before launch. We do not promise one figure for every site.
05 / DATA AND INFRASTRUCTURE
WHERE PROCESSING RUNS
IS PART OF THE TASK.
On-premise
We consider on-site processing when data locality and latency matter. We assess compute, network and operational resources.
Cloud or hybrid
We assess connectivity, availability, cost and what data may be transmitted. Camera processing and centralized analytics may have different requirements.
Storage and access
The specification defines recorded data, retention periods, roles, logging and access procedures. Personal data and observation zones are agreed before the pilot.
Integrations
ERP/MES/WMS, clinic systems and POS/KDS are possible integration areas. We assess interfaces, rights, versions and exchange rules. Each connector's readiness is confirmed separately.
06 / OPERATION
CONDITIONS CHANGE.
VALIDATION CONTINUES.
Monitoring
We define who monitors camera and processing availability, failure alerts and recovery times.
Changing data
A new angle, lighting setup, uniform or product can reduce performance. A control sample, validation and, where needed, adaptation are necessary.
Updates
We agree on rule and model versioning, pre-release validation and the ability to roll back.
Support
Responsibilities, service hours, response times and support scope are set out in the contract. Round-the-clock support is not assumed.
07 / LIMITS OF USE
SEE THE INDICATOR.
CHECK WHAT IT MEANS.
NEXT STEP / YOUR TASK
LET'S START
WITH YOUR PROCESS.
Busted is a computer vision integrator. We review your task for free and identify the data needed to scope a pilot. A paid pilot takes 14 days. Pricing depends on the number of cameras and monitored processes.
Prepare a description of the area, a sample recording without unnecessary personal data and the metric you want to improve.
info@busted.cv
Email us about your computer vision project.