Machine Vision: Vision Systems in Manufacturing

The initial stage involves capturing visual data using various imaging devices, such as industrial cameras (e.g., CCD or CMOS cameras) or specialized sensors. This raw image data often contains noise (e.g., shot noise, thermal noise) and environmental distortions, including shadows and reflections, which can impede subsequent analysis. Therefore, a crucial pre-processing step is undertaken to mitigate these issues. This may involve techniques like noise reduction filters (e.g., Gaussian blur, median filter), intensity normalization to compensate for uneven illumination, and masking to exclude irrelevant regions like strong reflections, which enhance image quality and reduce unwanted elements.

This data is then processed by algorithms and software to extract useful information — such as objects, shapes, or patterns — employing image processing techniques to enhance accuracy and adapt to complex scenarios, enabling tasks like object detection, recognition, and tracking. Examples are: image filtering, edge detection, blob analysis, and pattern recognition, including machine learning (such as convolutional neural networks – CNNs). Lighting also plays a crucial role, with techniques like backlighting and structured lighting being employed. Frame grabbers are often used to acquire and digitize the analog video signals from cameras for computer processing.

Following feature extraction, the machine vision system analyzes the extracted features to make decisions or initiate actions, such as automated optical inspection (AOI) for quality assessment, defect detection based on predefined criteria, or real-time monitoring for process control.  This stage involves applying algorithms, rule-based systems, statistical models, or machine learning classifiers to classify objects, measure dimensions, identify anomalies (defects), or determine spatial relationships for robotic guidance.

Based on this interpretation, the system can trigger specific actions, including pass/fail judgments, flagging defective parts, providing guidance data to robotic systems (e.g., pick-and-place), or adjusting parameters in automated processes for real-time monitoring and process control.
The resulting analysis can be visualized by human operators via graphical user interfaces (GUIs) or communicated directly to actuators, automated machinery (e.g., robotic arms, sorting mechanisms), and programmable logic controllers (PLCs) via control signals and industrial communication protocols to execute control commands or initiate reject signals. The interpreted data and resulting actions can also be logged for traceability and process optimization.