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Official guides and references to plan, build, and run Vision AI.
2D vs. 3D Vision: When Does Inspection Need Height and Depth Data?
02Visual AI for Wood and Furniture: Knots, Cracks, and Finish Defects
03AQL Sampling vs. 100% Inspection: When Should AI Cameras Take Over?
04AI Cameras for Ceramics and Glass: Cracks, Chips, and Bubbles
05Smart Inspection for Agricultural Sorting: Coffee Beans, Cocoa, and Fruit
06Machine Vision for Metal Surfaces: Scratches, Dents, and Rust
07Visual AI for Automotive Manufacturing: Welds, Paint, and Assembly Completeness
08Quality Inspection for Injection Molding: Short Shots, Flash, and Sink Marks
09AI Cameras for Warehouses and Logistics: Picking, Pallets, and Loading Docks
10Smart Inspection for Pharmaceuticals: Blisters, Vials, and Audit Trails
11Machine Vision on High-Speed Conveyors: Motion Blur, Triggers, and Timing
12Smart Inspection Pilot Roadmap: From Proof of Concept to Production in 30 Days
13Dimensional Measurement with Machine Vision: Calibration, Accuracy, and Tolerances
14Visual AI Model Drift: Why Inspection Accuracy Drops Over Time
15Datasets and Annotation for AI Quality Inspection: How Many Images Do You Need?
16Hardware Requirements for AI Cameras: Sizing PCs, GPUs, and Networks
17Smart Inspection for Textiles and Garments: Fabric, Stitching, and Color Defects
18Visual AI vs. Rule-Based Machine Vision: Which Should You Use?
19Retrofitting Existing CCTV with AI: When It Works and When to Replace It
20Choosing Cameras and Lenses for Industrial Machine Vision
21AI Quality Inspection Metrics: False Rejects, Escapes, and How to Measure Them
22Visual AI Inspection for Electronics: PCBs, Solder, and SMT Components
23Smart Inspection for Food and Beverages: Labels, Packaging, and Expiry Dates
24Lighting for Machine Vision: A Key Factor in Inspection Accuracy
25Integrating AI Inspection with PLCs, MES, and ERP: A Rule Engine Guide
26AI Anomaly Detection: Quality Inspection Without Defective Product Examples
27People Counting and Traffic Analytics with AI Cameras
28How to Build Your Own Vision AI Model Without Coding
29Choosing Visual AI Inspection Software for Your Factory
30AI Quality Inspection Costs and ROI: How to Calculate Investment Value
31OCR and ANPR with AI Cameras: Automatically Reading Text and License Plates in Industry
32AI Cameras for PPE Detection and Workplace Safety
33On-Premise vs. Cloud Vision AI: Which Fits Your Factory?
34AI Cameras for Product Defect Detection: An Implementation Guide
35AI Quality Inspection: How It Works and Its Benefits for Quality Control
36What Is Visual AI Inspection? A Complete Guide for Manufacturing
Do I need to know how to code?
Nirmana’s core workflow is visual: projects, datasets, annotation, training, inspection, rules, and integration. Camera setup and systems integration still require process knowledge and technical validation.
Which cameras can I use?
Documented sources include USB/webcams and IP cameras/CCTV with RTSP. Industrial cameras require verification of drivers and stream access; do not assume universal compatibility.
Does data have to be sent to the cloud?
Nirmana supports local/on-premise processing. Packages are available for Windows x64, Linux x86_64, and NVIDIA Jetson aarch64. Internet is required for activation and periodic license verification.
How is free mode different from a trial?
Without a license, Nirmana runs in a limited mode with one camera. Full-feature trials require an approved request and are time-limited. Trial activation is not automatic.
Can I connect PLC, MES, or ERP systems?
Documented integrations are Modbus TCP and HTTP/webhooks. All editions include PLC/API integration. Register mapping, payloads, and system workflows need engineering validation.
How accurate and fast is it?
Results depend on datasets, models, resolution, lighting, camera count, and hardware. Test representative data to establish feasibility. Website demo numbers are simulated, not product specifications.
Your camera can do more.
Build your Vision AI. Connect it to the real world.