What Computer Vision capabilities does VividLogic offer?

Our Computer Vision work covers Quality Inspection, Object Counting, Visual Anomaly Detection, and Edge Deployment.

Computer Vision

We build computer vision solutions for manufacturing, logistics, and retail. From quality inspection to inventory counting, our CV engineering delivers accurate, production-ready systems.

Our Approach

We assess your visual inspection needs and design CV solutions that solve your specific problems. Our computer vision includes model training, edge deployment, and integration with existing systems.

Capabilities

Quality Inspection

Automated defect detection on production lines.

Object Counting

Counting and inventory management with computer vision.

Visual Anomaly Detection

Identifying anomalies in images and video.

Edge Deployment

Deploying models to edge devices for real-time inference.

How It Comes Together

A typical AI system architecture

Data

Training and evaluation data sourced, labeled, and validated.

Model

Models trained, fine-tuned, or integrated via API.

Evaluate

Accuracy, cost, and latency benchmarked before release.

Deploy

Production rollout with guardrails and human oversight where needed.

Monitor & Retrain

Drift detection triggers retraining before quality degrades.

In Practice

Computer vision models that perform well on a curated test set and then fail on the factory floor almost always failed because the training data didn't represent real production conditions, lighting changes, camera angle drift, partial occlusion, that a curated dataset conveniently avoids. We collect and label training data from the actual deployment environment wherever possible, and when that's not available at launch, we build active learning into the system so it improves from real production examples over time rather than staying frozen at launch-day accuracy. Edge deployment gets evaluated as a first-class architectural decision, not an afterthought, since a quality inspection system whose latency depends on a round trip to the cloud is unusable on a production line moving faster than that round trip. We benchmark model size against the actual hardware constraints of the deployment target, from industrial edge devices to standard servers, rather than defaulting to the largest model that scored best in a research benchmark. Every computer vision system we ship includes a defined process for handling the false positives and false negatives that are inevitable at any accuracy below 100%.