Machine Vision Use Cases 2023
What is machine vision?
Machine vision is a field of computer science that enables machines to interpret and make decisions based on visual data. Using cameras and software, it automates tasks that require visual recognition and inspection. AI, particularly deep learning, enhances machine vision by allowing systems to train on vast amounts of data and recognize complex patterns, making visual recognition more accurate and adaptable.
According to IoT Analytics, there are four major use case categories (Inspection, Identification, Guidance, and Measurement) across 39 use cases. The top use cases are flaw detection, autonomous machines/robots, and process/operation optimization. The average machine vision project amortization is 16.8 months, or ~20% faster than the overall industry average of 20.1 months for the 17 major Industry 4.0 technologies.
Three Notable Machine Vision Technology Shifts
Advanced Cameras: Modern cameras exceed 45 megapixels, with event-based sensors improving imaging in low-light conditions.
Better Decision-Making with AI: The transition from rule-based to AI-based vision provides enhanced accuracy and flexibility in defect detection.
Powerful Hardware with AI Chips: Next-gen chipsets, optimized for AI, have dramatically reduced deep learning training times and are now embedded in smart cameras.
Seven Applications on the Rise
Flaw Detection: Modern AI-driven machine vision can detect anomalies without needing a database of specific defects, enhancing quality inspection in manufacturing.
Process Optimization: Enhanced cameras and AI enable robots to perform complex tasks with higher precision and efficiency, optimizing manufacturing operations.
Autonomous Driving: Advanced machine vision systems are crucial for developing fully autonomous vehicles, pushing the boundaries from driving assistance to full automation.
Pallet Dimensioning: 3D time-of-flight cameras measure loaded pallets accurately, reducing manual measurements and potential carrier charges in logistics.
Body Posture Analysis: High-precision cameras now allow for detailed analysis of body posture and movement, benefiting healthcare and ergonomic applications.
Automatic Checkout: Machine vision-based checkout solutions reduce wait times and increase transaction efficiency in retail stores.
Contaminant Identification: AI-powered machine vision effectively identifies contaminants in food products, ensuring higher quality and safety in the food industry.
References:
IoT Analytics Machine Vision Report 2022-2027: https://iot-analytics.com/top-7-upcoming-machine-vision-applications/#:~:text=IoT%20Analytics%20expects%20the%20market,difficult%202023%20for%20technology%20markets).
