Today, manufacturers and logistics facilities across all industries are leveraging the combined power of AI and machine vision to gain a competitive edge. By concurrently deploying these technologies, companies can better automate, optimize their efficiency, and improve quality control.
AI augments rule-based machine vision with image-based analysis. When a vision system receives an image, AI 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 reference images allows vision systems to differentiate between acceptable and unacceptable anomalies in objects under inspection.
Moreover, machine vision solutions embedded with AI technology require less extensive programming and technical expertise compared to rule-based approaches. Two leading technologies within AI – edge learning and deep learning – help to further simplify automation of highly variable tasks and solve tasks that are too complicated and time-consuming to program with rule-based algorithms.
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.
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.
