Train defect detection models from example images — no coding required

Cognex's ViDi Suite uses deep learning to learn from labeled images of good and defective products. Engineers simply provide examples, and the model learns to identify anomalies, classify defects, and locate features. This eliminates the need for hand-coded rules that break when product variations change.

Faster deployment and higher accuracy on complex, variable defects compared to traditional rule-based vision.

Inspect thousands of parts per minute without slowing the line

Cognex systems process images at production-line speeds — up to 10,000 parts per minute on certain models. The hardware is designed for factory-floor conditions: vibration, temperature extremes, and dust. This means quality checks happen inline without adding cycle time.

100% inline inspection at full production speed, eliminating the need for offline sampling or slower manual checks.

Read barcodes, datamatrix codes, and labels at high speeds with near-perfect accuracy

Cognex's barcode readers handle damaged, distorted, or low-contrast codes that other readers miss. The AI-based decoding algorithms compensate for poor printing, smudging, or lighting variations. This is critical for traceability in automotive, electronics, and pharmaceutical supply chains.

Reliable code reading that ensures product traceability and compliance with industry regulations.

Connect directly to PLCs, robots, and reject mechanisms for automated defect removal

Cognex systems communicate via standard industrial protocols (EtherNet/IP, PROFINET, OPC UA) and can trigger reject mechanisms, stop the line, or log data to a central database. This enables fully automated quality control without human intervention.

Seamless integration into existing production lines, enabling closed-loop defect removal and real-time data logging.

Track defect trends, yield rates, and line performance with built-in dashboards

Cognex's software suite includes dashboards that display real-time defect rates, yield trends, and historical data. Quality teams can identify recurring issues, correlate defects with production parameters, and drive continuous improvement initiatives.

Data-driven quality decisions that reduce scrap, improve yield, and lower overall production costs.

Deploy the same vision models across multiple lines and facilities globally

Once a model is trained and validated on one line, it can be deployed to other lines or plants with minimal rework. Cognex provides centralized management tools to update models, monitor performance, and push changes across the entire fleet.

Consistent quality standards across global operations, reducing variability between plants and shifts.

• AI-native deep learning handles variable defectsCognex's deep-learning models detect subtle anomalies like scratches, dents, or color variations that rule-based systems miss.
• Proven at high production speedsSystems inspect thousands of parts per minute without adding cycle time, enabling 100% inline inspection.
• Deep industrial integrationNative support for PLCs, robots, and reject mechanisms means Cognex fits into existing lines without custom middleware.
• Global scalability with centralized managementModels trained on one line deploy to any other line or plant, ensuring consistent quality worldwide.

• Enterprise pricing only — no self-serve or free tierCognex requires a sales engagement and six-figure budgets for multi-station deployments, making it inaccessible for small shops.
• Requires trained engineers for setup and model trainingDeploying Cognex systems demands vision expertise — not a plug-and-play solution for teams without machine vision experience.
• Not ideal for low-volume or highly customized productionThe upfront investment and setup effort are justified only for high-volume lines where defect costs are significant.
• Professional RealityCognex is a capital investment, not a SaaS subscription — budget for hardware, software, integration, and ongoing support.

Automotive assembly verification

Automotive manufacturers use Cognex to verify that components are present, correctly aligned, and free of surface defects on engine blocks, transmissions, and body panels. The deep-learning models detect missing bolts, misaligned gaskets, or paint imperfections that could lead to recalls.

Electronics PCB inspection

Electronics manufacturers inspect printed circuit boards for solder defects, component placement errors, and foreign object debris. Cognex's high-speed cameras and AI models catch defects at line speed, preventing faulty boards from reaching final assembly.

Pharmaceutical label and code verification

Pharma companies use Cognex to verify that labels, lot numbers, and expiration dates are printed correctly on vials, blister packs, and cartons. The AI-based code readers handle damaged or low-contrast codes, ensuring regulatory compliance.

Food and beverage packaging integrity

Food manufacturers inspect seals, fill levels, and label placement on bottles, cans, and pouches. Cognex systems detect leaks, underfills, or misaligned labels at speeds exceeding 1,000 packages per minute.

Identify the inspection application — define what defects you need to detect (surface, assembly, code, or presence).

Contact Cognex sales or an authorized integrator to discuss hardware requirements (camera resolution, lens, lighting, processor).

Set up a pilot station on one production line — train the deep-learning model using 50–200 labeled images of good and defective parts.

Validate model accuracy on live production, then deploy to additional lines and integrate with PLCs and reject mechanisms.