Computer Vision
I build computer vision systems for detection, classification, and measurement optimized for real-world constraints like edge devices, production environments, and low-latency requirements.
DetectionClassificationEdge AIDeploymentAutomation
Outcomes
- Automated inspection and quality control to reduce manual checking
- Faster and more consistent detection for operational reliability
- Edge deployment (Raspberry Pi / Jetson) or server-based inference
- API + UI delivery (not only a model in a notebook)
- Reproducible pipeline with documentation and clean handover
Common use cases
Defect detection & visual QC
Automate inspection to detect defects and reduce operator subjectivity.
Counting & measurement
Measure fill-level, presence/absence, and simple geometric metrics from images.
Edge AI deployment
Run inference on-device with low latency and offline capability.
API-ready CV systems
Expose inference through REST APIs and deliver web interfaces for users.
How I work
- Scoping + success metrics (what “good” looks like)
- Data review + feasibility (quality, edge cases, constraints)
- Prototype (PoC) to validate model + pipeline
- Iteration + evaluation (precision/recall, latency, failure modes)
- Deployment (API/UI/Edge) and environment hardening
- Handover + documentation (reproducibility, maintainability)
What you get
- Working web demo (upload → prediction → visualized results)
- REST API + docs (OpenAPI-ready)
- Model weights + versioned configuration
- Deployment guide (Raspberry Pi / server)
- Optional training and knowledge transfer
Featured Projects
Ready to talk?
Email me with a short summary of your use case. The more context you provide, the faster I can advise the best approach.
What to prepare
- Sample images/video (if available)
- Expected classes/labels
- Latency target
- Deployment environment (Pi/Jetson/Server)