The evolution of Industry 4.0 has brought sensors, robots and algorithms to production lines that mimic the way the human brain interprets its environment. Machine vision systems combine specialised cameras, lighting, depth sensors and artificial intelligence algorithms to capture images, analyse patterns and make decisions in milliseconds. By equipping machines with intelligent “eyes” capable of extracting relevant information, companies significantly raise standards of quality, productivity and safety.
What are machine vision systems and how do they work?
Machine vision differs from computer vision in that it aims to capture only the information required to trigger a specific response, whereas computer vision analyses the entire image in depth. On a production line, for example, machine vision focuses on detecting defects or measuring dimensions, rather than interpreting every element present in the image.
Industrial vision systems combine 2D and 3D cameras, sensors, controlled lighting and specialised software to identify patterns, compare parts against reference models and send decisions to robots or PLCs. The current processing power and advances in deep learning techniques explain the rapid evolution of this technology.
Key benefits of machine vision systems:
The main benefits of machine vision systems are widely documented and justify the growing industrial investment:
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- Precision and quality – increase inspection accuracy and eliminate human error, ensuring products meet specifications. 3D cameras enable the detection of microcracks, inclusions or deformations invisible to the human eye.
- Profitability and cost reduction – by detecting defects at an early stage, waste is reduced and rework or returns are avoided. Automation makes it possible to inspect 100% of production, even on high-speed lines, without compromising productivity.
- Safety and ergonomics – vision and AI systems monitor hazardous areas and replace repetitive manual inspections, improving safety and reducing physical strain on operators.
- Sustainability and energy efficiency – automation prevents material waste and reduces energy consumption, as equipment adjusts parameters based on reliable data.
- Flexibility – robots equipped with vision can switch tasks quickly and without the need for precise part positioning. Systems allow algorithms to be adapted to new references and hardware to be scaled according to production needs.
- Monitoring and traceability – when integrated with supervisory software, they provide real-time visibility into quality, enabling immediate intervention whenever anomalies occur.
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Quality control and inspection
In the plastics industry, Sentinel Vision, in partnership with Zebra Technologies, implemented an in-line 3D vision solution for the inspection of injection-moulded parts. The system captures the entire surface of each part, uses deep learning algorithms to identify micro-defects and integrates seamlessly into the production line. The results include:
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- A significant increase in defect detection and a reduction in late rejections.
- Reduction of waste and costs associated with rework.
- Maintained or increased productivity without creating bottlenecks.
- Improved traceability and data collection for continuous improvement actions.
- The ability to replicate the solution across other lines or machines.
2D/3D inspection across different industries
Machine vision can be applied to virtually any industrial sector. Common examples include the automotive industry, with automated weld inspection, dimensional measurements, code reading and support for pick-and-place robots. In metalworking, it is used for weld inspection, surface roughness detection and tolerance measurement. In the plastics industry, it enables dimensional measurement and the detection of inclusions or colour variations. In the food industry, it is applied to product counting, fill-level verification and packaging inspection. In the pharmaceutical sector, it is used for tablet counting and verification of seals or leaflets. Finally, in the glass industry, it enables the detection of cracks and defects throughout the production process.
Eliminating subjectivity in inspection
Vision systems ensure fully consistent inspection criteria, eliminating subjectivity and fatigue associated with manual checks. In addition, their flexibility allows new references to be created easily and hardware to be scaled according to production needs. They also enable 100% inspection at high speeds, reducing costs by avoiding complaints and returns.
3D vision and artificial intelligence for defect detection
The combination of 3D vision and AI is now essential to achieve the goal of “zero defects”. While 2D vision captures only intensity and texture, 3D vision provides metric information on shape, volume and topography. This makes it possible to detect:
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- Geometry variations and deformations
- Burrs, depressions and positional deviations
- Misalignments and gaps that are impossible to see in 2D
Structured light and laser triangulation sensors generate depth maps with sub-millimetre resolution and excellent repeatability. AI complements this information through neural networks that classify defects or anomaly detection models that learn the “normal” state and flag any relevant deviation. This approach reduces the need for large defect datasets and makes the system more robust to real production variations.
Vision Core – flexibility and low code
Sentinel Vision is not only a hardware supplier; it has also developed Vision Core, a high-performance platform focused on solving production challenges. The software is intuitive and flexible, adapting to customer images and requirements. It includes inspection, measurement and identification tools based on the most advanced AI algorithms. Key features include:
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- integrated artificial intelligence modules
- support for multiple peripherals within a single environment
- compatibility with 2D and 3D cameras
- integration with Halcon
- low-code drag-and-drop interface
- generation of key statistics for continuous improvement
- preparedness for Industry 4.0 requirements
This approach allows engineering teams to build complex systems with significantly less programming and a greater focus on efficiently solving inspection challenges.
Conclusion
Machine vision systems have become a fundamental element of Industry 4.0. With high-resolution cameras, 3D sensors and AI algorithms, they ensure rigorous quality control, reduce waste and increase productivity. Companies that invest in this technology benefit from more accurate, flexible and faster inspections, strengthening the competitiveness and sustainability of their processes.
With integrated low-code hardware and software solutions, Sentinel Vision positions itself as a strategic partner for organisations seeking excellence and innovation in manufacturing.