Map industrial vision capability to real production scenarios.
This page explains how AVIA fits flexible assembly, 3C workstations, defect inspection, sort and pack, lab automation, and multi-camera monitoring.
Labels
- Defect24
- Part57
- Weld seam12
- Anomaly3
Business layer
Scenario, station, task
Technical layer
Data, label, train
Operations layer
Review, deploy, feedback
- 01
Field data
Camera, mobile, S3
- 02
Task definition
Defect, pose, OBB
- 03
Review queues
Human + AI
- 04
Train and evaluate
YOLO / AD
- 05
Deploy and monitor
Edge events
- 06
Retrain
Failure return
Six industrial vision scenarios, from station to field feedback.
- 6
- Scenarios · assembly, defect, sort-pack, lab, 3C, multi-camera
- 5
- Task types · detect, segment, pose, OBB, AD
- 3
- Field camera protocols · RTSP, ONVIF, GB28181
Scenarios from station data to model feedback.
Each scenario starts from the business problem and maps to AVIA's data-loop capabilities.
Flexible assembly and station verification
Adapt to product variants, assembly steps, and fixture changes by using active learning to fill new sample gaps.
Defect and anomaly inspection
Scratches, stains, missing parts, anomalous textures, and rare defects flow into quality analysis, human review, and AD training.
Sort, pack, and high-speed inspection
High-speed sorting, packaging checks, label recognition, and missed detections continuously return to the data loop.
Lab automation
Experiment samples, instrument states, and visual records automatically move into annotation, review, and training workflows.
3C workstations and small-object detection
Multi-station, multi-class, small-object, and oriented-object tasks are managed through unified dataset versions.
Multi-camera monitoring and field feedback
RTSP, ONVIF, GB28181 cameras, preview, capture, and events can return to datasets.
Move from scenario to data loop in clear stages.
Scenario deployment is not one model training run. It is ongoing operation of data, review, and model versions.
Define scenario
Define station, object, defect, acceptance criteria, and capture method.
Connect data
Connect mobile, camera, or BYOK data sources.
Select samples
Use active learning and quality metrics to choose the first review set.
Train and evaluate
Train models and improve data through failure analysis.
Production feedback
Collect edge events, false positives, and false negatives after deployment.
Retraining
Drive the next retraining cycle with DatasetVersion and Collections.
Deployment principle
The same platform carries the scenario language for business teams and the data/model language for engineering teams, reducing handoff loss.
Turn your production scenario into an operational data loop.
Share your station, data source, and target task. We will map it into an AVIA workflow.