Key Takeaways
Here’s a quick look at how innovative machine vision and AI solutions are driving results for modern manufacturers and logistics teams:
- Edge learning: Processes on-device, requires minimal training, and is ideal for quick, simple inspections.
- Deep learning: Handles complex applications with large datasets and advanced neural networks.
- AI-powered vision: Detects subtle defects and optimizes quality control with minimal programming.
- Ease of use: Simplifies label reading and inspection setup with natural language processing.
- Competitive advantage: Combines AI and machine vision to enhance automation and operational efficiency.
AI augments rule-based machine vision with image-based analysis. When a computer (or vision system) receives an image, AI-powered software compares that image with a database consisting of both “good” and “bad” reference images and outputs a result. This process of learning to recognize patterns and infer from annotated images allows computers to detect defects by differentiating between acceptable and unacceptable anomalies in objects under inspection.
Moreover, machine vision solutions embedded with AI technology use natural language processing to read and interpret labels on images, compared to rule-based approaches which require extensive programming and significant technical expertise. This enables a wider base of users to take advantage of AI for factory automation. Two leading technologies within AI – edge learning and deep learning – help to further simplify automation.
Edge learning – Edge learning is an AI technology designed for ease of use. Processing takes place on-device, or “at the edge,” using a pre-trained set of algorithms. The technology is simple to setup, requiring smaller image sets (as few as 5 to 10 images) and shorter training periods than traditional deep learning-based solutions. Needing no domain expertise, non-vision experts can train edge learning tools and generate inspection results in minutes.
Deep learning – Deep learning is an AI technology designed to automate complex and highly customized applications. Processing takes place via a graphics processing unit (GPU), which enables users to build sophisticated neural networks from large, detailed image sets (hundreds to thousands of images). Leveraging these neural networks, deep learning quickly and efficiently analyzes vast image sets to detect subtle, variable defects.
Learn more about these technologies and how AI is being used to give manufacturers a competitive edge in the infographic below.