Comparación de métodos de inspección: manual, visión artificial y Edge Learning
A la hora de realizar inspecciones visuales, los fabricantes pueden seguir varias estrategias, como un método manual (inspección humana), visión artificial (algoritmos basados en reglas) y Edge Learning (IA). Estas estrategias no son exclusivas entre si y pueden emplearse de modo simultáneo, pero cada una ofrece su propio conjunto de beneficios y retos.
Human visual inspection is well-suited for situations that require learning by example and appreciating acceptable deviations from the control. Unlike traditional, rule-based machine vision, humans are adept at distinguishing between subtle cosmetic and functional flaws, as well appreciating variations in part appearance that may affect perceived quality. Though limited in the rate at which they can process information, humans are uniquely able to conceptualize and generalize. Humans excel at learning by example and are capable of distinguishing what really matters when it comes to slight anomalies between parts. This makes human vision the best choice, in many cases, for the qualitative interpretation of a complex, unstructured scene—especially those with subtle defects and unpredictable flaws.
Simply put, machine vision gives computers and industrial equipment the ability to “see” what it is doing and make rapid decisions based on what it sees. The most common uses of machine vision are visual inspection and defect detection, positioning and measuring parts, and identifying and sorting products. It is one of the founding technologies in factory automation and has helped to improve product quality, speed production, and optimize manufacturing operations for decades.
Machine vision excels at the quantitative measurement of a structured scene because of its speed, accuracy, and repeatability. A machine vision system built around the right camera resolution and optics can easily inspect object details too small to be seen by the human eye and inspect them with greater reliability and less error. On a production line, machine vision systems can inspect hundreds or thousands of parts per minute reliably and repeatedly, far exceeding the inspection capabilities of humans.
Edge learning brings together the best of traditional, rule-based machine vision and artificial intelligence. In this way, edge learning combines the flexibility of human visual inspection with the speed and robustness of a computerized system. It introduces a level of simplicity not found within deep learning-based solutions to deliver an easy-to-deploy method for factory automation. With no technical experience required, anyone can use edge learning to solve a wide range of manufacturing tasks and automate inspections.