Confidential CV systems work

Computer Vision Systems

A sanitized case study on production computer vision work across segmentation, image-quality checks, annotation workflows, model evaluation, and backend data debugging.

Customer identity, product specifics, internal workflows, datasets, and implementation details are intentionally generalized.

91%segmentation validation performance

~30%capture-readiness improvement

~24%pipeline consistency improvement

1,600+reviewed examples

Overview

Problem

Real-world computer vision inputs are messy. Glare, blur, framing, positioning, and inconsistent captures can all make model output harder to trust.

My role

I worked across the model/data boundary: reviewing outputs, preparing datasets, improving validation checks, debugging production cases, and helping make results more repeatable.

Selected work

Built and tuned OpenCV quality checks for blur, glare, distance, framing, and capture usability.
Helped prepare and review segmentation datasets for model training and validation.
Debugged model output by tracing image assets, metadata, database records, and evaluation results.
Identified and fixed a capture-sorting issue that improved a downstream processing workflow.
Reviewed large sets of labeled examples and helped convert edge cases into clearer annotation and validation rules.

Stack

PythonOpenCVYOLO segmentationCVATPostgreSQLdata validationmodel evaluationannotation workflowsCLI tooling