Deep learning in the context of industrial machine vision teaches robots and machines to do what comes naturally to humans, i.e. to learn by example. New multi-layered “bio-inspired” deep neural networks allow the latest IVS® machine vision solutions to mimic the human brain activity in learning a task, thus allowing vision systems to recognise images, perceive trends and understand subtle changes in images which represent defects.
Where appropriate, IVS® machines utilise deep learning and artificial intelligence back propagation networks for the classification of image and object characteristics in automated inspection. Over the last 20 years, we have provided machines which have benefited from the advancements in neural network image processing to give more precise and repeatable solutions, which can “learn” based on the more the vision system sees.
IVS® deep learning solutions utilise the very latest format neural networks. This provides our machine vision solutions with the ability to “learn by example” and improve as more data is captured. Deep learning combines the flexibility of the human visual system with the consistency and dependability of a computer system. Deep neural networks can continuously refine their performance as they are presented with additional images and information.
Artificial intelligence (AI) utilising deep learning will be a crucial technology moving into the 2020s. The move to Industry 4.0 flexible manufacturing makes this key technology even more critical in today’s modern factory. IVS can help solve your manufacturing problems using our in-depth and extensive knowledge of this critical technology.
Machine vision performs well at the quantitative measurement of a highly structured scene with a consistent camera resolution, optics and lighting. Deep learning can handle defect variations that require an understanding of the tolerable deviations from the control medium; for example, where there are changes in texture, lighting, shading or distortion in the image. Our deep learning vision systems can be used in surface inspection, object recognition, component detection and part identification. AI deep learning helps in situations where traditional machine vision may struggle, such as parts with varying size, shape, contrast and brightness due to production and process constraints.