The Small 1x1 of Machine Vision
Technologies & Trends in Machine Vision
Staying Ahead in a Fast-Moving Market
Machine vision is one of the most rapidly evolving technologies in industrial automation. New developments in AI, 3D sensing, embedded processing and spectral imaging open up unprecedented possibilities for quality control, traceability and autonomous production. STEMMER IMAGING helps businesses turn these trends into reliable, high-performing solutions.
AI & Deep Learning: Smarter Inspections
Rule-based image processing remains the backbone of many production applications - but artificial intelligence is expanding what machine vision can inspect and how systems are trained. Deep learning now handles tasks where visual features are complex, variable, or difficult to define with classical algorithms.
Defect detection on textured or reflective surfaces, anomaly classification without predefined defect types, and flexible object recognition in unstructured environments are all areas where AI-based approaches produce results that traditional rule sets cannot reliably achieve. AI models learn from labelled image data rather than hand-coded logic, which reduces the engineering time needed to deploy and adapt inspection systems.
STEMMER IMAGING supports AI-based vision applications through its own software platform Common Vision Blox (CVB), and through application expertise across deep learning frameworks and edge deployment.
3D Vision: Seeing in Depth
- Laser triangulation for high-precision profiling
- Stereo vision for flexible bin picking
- Time-of-flight sensors for real-time 3D data
Embedded Vision & Edge Computing
- Compact, fanless design
- Real-time processing close to the sensor
- Lower total system cost
Spectral Imaging & Non-Visible Insights
- Food sorting
- Pharmaceutical verification
- Electronics and solar cell inspection
Factory Connectivity & Vision 4.0
- OPC UA and MQTT communication standards
- Remote diagnostics and predictive maintenance
- Centralized data handling and dashboard visualization
MORE Services for New Technology Adoption
- Technology evaluation, PoC development, architecture consulting
- System rollout, AI model deployment, training and lifecycle support
- Edge platforms, cloud integration, hybrid AI + rules-based systems
Frequently Asked Questions: Technologies & Trends in Machine Vision
What role does AI play in modern machine vision?
Artificial intelligence - particularly deep learning - has significantly expanded what machine vision can reliably inspect. Where rule-based systems struggle with highly variable surfaces, organic shapes, or subtle defects, deep learning models learn from examples and adapt to natural variation. Common AI applications include anomaly detection on surfaces, flexible OCR across distorted fonts, and object classification in unstructured environments. AI does not replace classical machine vision; it extends it - adding capability where rigid rules are insufficient, while classical algorithms remain faster and more deterministic for well-defined tasks.
What is embedded vision and how is it different from PC-based machine vision?
Embedded vision processes image data on compact, application-specific hardware rather than on a separate industrial PC. Typical embedded vision systems use a single-board computer, an FPGA, or a system-on-chip that combines image acquisition and processing in a small, low-power package. This is particularly valuable in mobile machines, collaborative robots, and distributed factory environments where space, power, and connectivity are constrained. PC-based systems offer more processing headroom and are easier to update, making them better suited to complex or frequently changing inspections.
What is 3D machine vision used for?
3D machine vision captures the spatial geometry of an object, not just its 2D appearance. This enables volume measurement, surface profiling, height inspection, and robot guidance tasks where a flat image would be insufficient. Common 3D technologies include laser triangulation for high-precision surface profiling, time-of-flight sensors for rapid scene capture, and stereo vision for flexible robot guidance. 3D vision is increasingly used in bin picking, robotic assembly, packaging quality control, and weld inspection.
What is spectral imaging and when is it relevant?
Spectral imaging captures information beyond the visible spectrum. Hyperspectral cameras record data across many wavelengths simultaneously, revealing material composition, moisture content, or surface coatings that are invisible to standard cameras. SWIR (short-wave infrared) cameras are used to detect contamination in food, inspect pharmaceutical tablets, or identify material types in recycling. Spectral imaging adds cost and complexity, but for applications where standard imaging fails to distinguish what matters - material rather than appearance - it provides decisive information.
How is machine vision connected to Industry 4.0 and factory IT systems?
Modern machine vision systems are no longer isolated inspection tools. Through standards such as OPC UA and MQTT, vision systems can communicate inspection results, statistical process data, and system status to MES, ERP, and cloud platforms in real time. The OPC UA Companion Specification for Machine Vision (OPC Machine Vision), developed by VDMA, specifically addresses this, enabling standardised data exchange between vision systems and the wider factory IT infrastructure. This connectivity supports predictive quality, traceability, and process optimisation across the production line.
Is deep learning replacing classical machine vision algorithms?
No - deep learning complements classical machine vision rather than replacing it. Classical algorithms remain the preferred choice for deterministic, high-speed tasks: measuring a dimension, reading a data matrix, checking a binary feature. They are fast, transparent, and require no training data. Deep learning adds value where variation is high, defects are complex, or the rules are difficult to define explicitly. Most production systems combine both: classical processing for structured tasks, AI for the elements that resist rule-based definition.