By using intelligent computer vision technologies, companies gain precise insights into areas that the human eye can neither capture nor process. Be it inside complex systems, detecting the tiniest deviations or monitoring high-speed manufacturing processes. Where human vision fails, the data-driven factory, combined with AI vision, enables companies to achieve ambitious OEE (Overall Equipment Effectiveness) targets and secure competitive advantages.
A prime example of AI solutions paired with cameras is quality inspection. Other KPI-relevant applications include process control in complex settings, resource optimization and automated predictive maintenance.
Computer vision enables machines to interpret and analyze real-world visual data. When powered by artificial intelligence, it becomes AI Vision—employing image processing technologies, customized algorithms and learning models capable of recognizing and evaluating patterns, shapes, and anomalies in image data.
A key component of AI Vision is the use of machine learning (ML), particularly deep learning. A neural network is trained with large amounts of images, showing both flawless and defective components, enabling the model to distinguish typical defects from normal variations. This model is then deployed on the production line, where it analyzes image data in real-time and automatically triggers an action upon detecting a defect. Through the seamless integration of high-precision image processing and advanced ML, AI Vision significantly enhances quality control.
Whether for retrofitting existing production lines or deploying in new plants, AI Vision requires all components to work seamlessly together.
Image data serves as the foundation for AI Vision projects. To succeed, companies must ensure their data collection, storage, and processing adhere to modern standards to meet performance requirements. In an industrial setting, all relevant process data needs to be captured and analyzed in real time. A strong data infrastructure lays the foundation for businesses to fully unlock the benefits of AI Vision for industrial applications like automated defect detection, identification of faulty parts, predictive maintenance, and error diagnosis using vector search.
Intelligent image recognition can unlock many use case scenarios in in industrial production to optimize processes, improve quality and reduce production downtime. Most important application areas are quality control, component recognition and sorting, and predictive maintenance.
AI Vision enables non-destructive inspection of quality parameters. The technology detects tiny defects on components that are barely visible. High-resolution cameras or specialized sensors, paired with calibrated lighting techniques, capture detailed images of the components. These images undergo a preprocessing stage, where contrast enhancement and noise reduction are applied to reveal even the smallest defects. Machine learning models process the collected image data reat-time to recognize complex patterns, extract specific features, and classify various defects. Typical applications include crack detection, verification of geometric dimensions, and identification of deformations and contamination. Color deviations and component completeness can also be reliably monitored.
Another interesting application area is automatic component recognition and sorting. In industrial production and assembly, various parts can be identified based on their shape, size, serial numbers, and specific codes. AI Vision allows for precise recognition of complex component features, ensuring efficient sorting and processing. The technology also enhances component tracking and precise placement, such as in automated bin picking. This not only accelerates production but also reduces errors that could occur from manual intervention. AI Vision can take the automation of production processes to a new level.
AI Vision can also play a key role in improving production metrics through predictive maintenance. Intelligent vision systems can detect signs of wear early—whether on machines, equipment, or critical components. The required equipment can be easily retrofitted without disrupting ongoing operations. Like in visual quality control, detailed images and measurement data are captured to reveal even the smallest changes in surfaces or structures. The technology can identify minimal signs of damage, foreign objects, fluid leaks, or corrosion in real time and spotting trends before they escalate into major issues. As a proactive approach to maintenance, AI vision can help reducing downtime and extend the lifespan of production machinery.
Device Insight is your trusted partner to support the entire process of developing and implementing your custom AI Vision solution—from concept development and consulting to ROI assessment, full implementation, and monitoring. Our engineers help through the selection and setup of use case specific hardware for image capture and lighting as well as with connecting image data, generating synthetic image data, model training, and selecting and providing a suitable cloud-based infrastructure. Another key element is the integration of dashboarding and alerting functions, enabling users to monitor the status of their production processes in real time and intervene immediately if deviations occur. Our holistic approach ensures that clients can fully maximize the benefits of their AI Vision investments.
"AI Vision is one of my favorite Industrial AI applications. Even on a small scale project, it can provide huge benefits."
Learn how companies can overcome data silos, cost traps, and complexity barriers when implementing data analytics
Delivering excellence in IoT. We are an IoT Solution Provider for Smart Products, Connected Vehicles, Smart City, Smart Energy and Smart Production.
