Computer Vision Quality Inspection
Turn cameras, drones, thermal sensors, and edge devices into real-time quality-control workflows with model scoring, exception handling, and audit-ready evidence.
90%+
impurity detection accuracy in the SMBSC AWS case study
3 sec
or less to grade each incoming beet load
$5M
potential annual savings projected by SMBSC
Where It Fits
- Agricultural intake grading and crop quality scoring
- Food and CPG packaging, label, cap, fill-level, and foreign-object inspection
- Manufacturing defect detection on production lines
- Power grid drone and thermal asset inspection
- Construction progress, safety, and defect review from site imagery
- Defense vehicle readiness, logistics QA, and edge-deployable inspection
How It Works
- 1Capture images from cameras, drones, mobile devices, thermal sensors, or edge devices
- 2Detect defects, impurities, anomalies, missing components, or quality classes
- 3Score each item, load, asset, or image against agreed thresholds
- 4Escalate exceptions into alerts, tickets, dashboards, or human review queues
- 5Store images, scores, timestamps, and reviewer actions as audit evidence
- 6Use reviewed examples to improve model performance over time
AWS-Native Inspection Architecture
Tactical Edge designs computer vision systems around the operating workflow, not just the model. Typical architectures use Amazon S3 for image storage, AWS Lambda for event-driven processing, Amazon SageMaker AI for model training and inference, and dashboards or ticketing integrations for action.
Pilot Path
Step 1
Pick one line, asset, site, crop, device, or inspection workflow
Step 2
Choose one repeatable camera setup and image-capture pattern
Step 3
Define 3-5 defect classes or quality indicators
Step 4
Set the pass, fail, review, and escalation workflow
Step 5
Measure precision, recall, inspection time, manual review reduction, and avoided loss
Start with one repeatable inspection bottleneck and a measurable operational KPI.
Schedule a Visual QA Assessment