Computer Vision Solutions for Inspection, Recognition and Analytics

Computer vision solutions use cameras and AI models to extract information from images and video automatically, such as detecting defects, reading labels, counting objects, recognising people and vehicles or spotting safety violations. They run on edge devices or cloud and integrate with MES, WMS, ERP and security systems.

Cameras and controlled lighting capture images of each product. A trained model classifies the image or locates defects and outputs a decision in milliseconds. The result triggers a reject mechanism or alert through the PLC and is logged with the image for traceability and quality analytics.

It depends on the technique. Classification and detection models typically need hundreds to a few thousand labelled images per class across conditions; anomaly detection can start with mostly good-part images when defects are rare. We plan data collection during feasibility and keep improving the model with production images.

Production inspection systems routinely achieve detection rates above 98 percent with low false rejects when imaging is well designed and models are trained on representative data. We agree target miss and false-reject rates up front, validate against held-out data and measure on the live line before sign-off.

Yes. OCR models read printed batch, lot, expiry and MRP text, dot-matrix and inkjet codes, nameplates and multilingual labels, and can be trained on handwriting styles found on your forms. Results are verified against expected values from ERP or production orders.

Inline inspection and safety alerts need low latency and run on edge devices such as NVIDIA Jetson or industrial PCs, keeping images on premises. Cloud is suited to batch processing, model training and multi-site dashboards. Most deployments are hybrid.

Yes. Decisions are sent to PLCs over digital I/O or industrial protocols to trigger rejects and andons, and results are posted to MES, SCADA, WMS or ERP through APIs. Images and metadata are stored for traceability and quality reporting.

Traditional machine vision uses rule-based image processing that works well for fixed, high-contrast tasks but struggles with variation. Computer vision with deep learning learns from examples, handling textured surfaces, natural products, varied lighting and text. Many systems combine both: rules for measurement, AI for defects and recognition.

A feasibility study with sample images takes two to four weeks. A production pilot including imaging setup, data collection, model training, validation and integration typically takes eight to twelve weeks, after which the solution is replicated to additional lines or sites.

Cost depends on the number of inspection points or camera streams, imaging hardware, the complexity of the detection task, data labelling effort, edge compute and integration scope. A feasibility study is an inexpensive way to confirm viability before committing to a pilot. Share your use case and samples for a detailed estimate.