Computer vision for aircraft inspection & navigation
From surface defect detection to assembly verification. Build, deploy, and monitor vision AI models that meet aerospace-grade requirements.
Defects Detected
- 12,400+
Model Accuracy
- 98.2%
Inspection images processed
- 5M+
Defect detection accuracy
- 98%
Faster inspections
- 70%
Missed critical defects
- 0
One platform for your entire inspection AI stack
Picsellia connects hangar floor cameras, drone feeds, and borescope imagery with centralized data management, model training, and deployment.
Picsellia MLOps Platform
Centralized orchestration layer
Data Management: Centralize & version
Annotation: Label & review
Training: Build & experiment
Deployment: Ship & monitor
Inspection Sources
- 4 CONNECTED Devices Running Inference at the Edge
- Hangar Cameras: Fixed high-res inspection stations (Latency< 30ms)
- Drone Inspection: Autonomous surface scanning (Latency< 50ms)
- Borescope Feeds: Engine internal inspection (Latency Real-time)
- Satellite Imagery: Airfield & facility surveys (Latency On-demand)
Continuous improvement: Inspection data → Annotation → Retraining → Deployment
Built for aerospace operations
From MRO hangars to production lines, Picsellia powers visual inspection workflows across the aerospace industry.
Aircraft Surface Inspection
- Detect cracks, dents, corrosion, and paint damage on fuselage and wing surfaces. Replace time-consuming walkarounds with automated visual analysis.
Component Wear Analysis
- Monitor turbine blades, landing gear, and hydraulic systems for signs of fatigue or wear. Predict maintenance needs before failures occur.
Foreign Object Debris (FOD)
- Detect debris on runways, taxiways, and in hangars that could cause damage to engines or airframes. Real-time alerts for ground crews.
Assembly Verification
- Verify correct assembly of complex components, wiring harnesses, and fastener patterns. Ensure compliance with engineering specifications.
From raw imagery to certified AI
Four steps to transform inspection data into automated defect detection with full traceability.
Collect
- Centralize all inspection imagery
- Aggregate images from hangar cameras, drones, borescopes, and manual inspections into a single versioned repository with full metadata.
- Connect any storage (S3, Azure, GCS)
- Auto-extract metadata & tail numbers
- Search by aircraft, zone, or date
- Version your datasets automatically
Annotate
- Label defects with your engineers
- Your MRO engineers know what a fatigue crack looks like. Capture that expertise by labeling defects in images. AI pre-annotation accelerates the process by 10x.
Train
- Build traceable detection models
- Turn labeled inspection data into AI models with full experiment tracking. Every dataset version, hyperparameter, and result is logged for auditability.
Deploy
- Ship models to edge devices in hangars, on drones, or via cloud APIs. Monitor performance in real-time and retrain when accuracy drifts.
The result? Inspection AI that meets aerospace standards.
- 10x Faster labeling with AI assistance
- 98% Defect detection accuracy
- < 50ms Edge inference latency
- 100% Audit traceability
Built for aerospace-grade requirements
Aerospace operations demand the highest levels of security, traceability, and compliance. Picsellia is designed to meet these standards from day one.
ISO 27001 Certified
- Enterprise-grade security and data protection
On-Premise Deployment
- Keep sensitive inspection data within your network
Air-Gapped Support
- Operate in fully disconnected environments
Full Audit Trail
- Every action logged for regulatory compliance
Role-Based Access
- Fine-grained permissions for teams and contractors
Security First
- Your inspection data stays protected
- AES-256 Encryption
- 99.9% Uptime SLA
- EU/US Data Residency
- Daily Backup
Ready to automate your inspection workflows?
See how aerospace companies use Picsellia to detect defects faster and reduce aircraft downtime.
5M+ Images analyzed
<50ms Edge inference
99.9% Uptime