Edge Learning in Machine Vision Systems

Edge learning is ideal when deployment speed matters and when the inspection criteria are relatively stable. If a production line frequently changes SKUs or if it is working with consistent product orientation, edge learning can give manufacturers the ability to move quickly without sacrificing performance. Edge machine learning allows:

• On-device processing (no cloud reliance)
• Real-time decision-making
• Faster setup and ROI

• Food & Beverage: e.g., classifying tray contents on frozen meals and label placement
• Electronics: managing frequent design changes, new parts, and identifying defects
• OEMs and System Integrators: rapid feasibility studies and deployment
• Automotive: catching defined defects on complex materials where the color, reflection, and texture may not always be consistent

Deep learning becomes necessary when inspection complexity exceeds what traditional or edge systems can handle. This includes detecting cosmetic flaws that vary widely, verifying assemblies with irregular part geometries or working in environments where lighting and orientation introduce variability that simpler systems can’t resolve.

The caveat is that it needs thousands of images to work effectively, and significantly increases the deployment time, which disqualifies it from many applications where time and money are a priority.

Edge learning on the KEYENCE VS Series smart camera bridges the gap between high-performance vision and easy deployment. With edge AI running directly on the device, the VS Series delivers fast learning cycles, minimal setup, and robust inspection accuracy—all within a compact, scalable solution.

Number of images required for learning before operation

Edge machine learning systems are often deployed with smart cameras, which integrate sensors, processors, and I/O into a single unit. They can use as few as five to 10 images, can be configured by operators or engineers (non-experts), and deployed within hours or days.

The VS Series is a good example, combining edge learning tools with flexible connectivity. These cameras typically support industrial protocols such as Ethernet/IP™ or PROFINET, making it easier to connect with PLCs, HMIs, or other control devices without needing a separate processing unit.

Most edge-enabled vision systems come bundled with software that supports drag-and-drop model creation, quick retraining, and no-code interfaces. These platforms are designed to work with minimal setup and are often compatible with broader automation software environments, which maintain production speed and flexibility.

Deep learning systems, on the other hand, may require external training or cloud-based tools to develop and deploy their models.

Edge learning systems are designed for speed, with training typically completed within minutes and models running in real time. In contrast, deep learning requires longer training time, ranging from several hours to days, depending on image set size, model architecture, and compute resources.

Deep learning typically requires more structurally sound infrastructure for both training and inference, often involving GPU-equipped servers or cloud access, unless they are optimized with techniques in quantization or knowledge distillation. Edge learning is lightweight; a smart camera with onboard processing is usually enough.

Deep learning excels at identifying subtle or highly variable defects, especially when the visual criteria are hard to define manually. However, the learning curve is steep, and model management is more involved.

Edge learning dramatically lowers both the technical and financial barriers to entry. It simplifies system architecture and reduces the need for external infrastructure, cutting down on both CapEx and OpEx.

With minimal training data and no need for a specialist to tune the model, manufacturers can troubleshoot or update inspections without pulling in a data science team, keeping lines moving with minimal interruption.