Deep learning teaches machines to do what comes naturally to humans: to learn by example. New, low-cost hardware has made it practical to deploy a multi-layered “deep” neural networks that mimic neuron networks in the human brain. This gives manufacturing technology amazing new abilities to recognize images, distinguish trends, and make intelligent predictions and decisions. Starting from a core logic developed during initial training, deep neural networks can continuously refine their performance as they are presented with new images, speech, and text.
how to implement deep learning procedure layer by layer
Breakdown of the deep learning procedure layer-by-layer (Source)
So what is Machine Vision then ?
Machine vision is the technology and methods used to provide image-based automatic inspection. technology glasses is a system that uses visual computing technology to mechanically “see” the activities that take place one by one along the production line. The components of an automatic inspection system usually include lighting, a camera or other image acquiring device, a processor, software, and output devices.
machine vision warehouse 3d images
Machine vision surpasses human vision at the quantitative and qualitative measurement of a structured scene because of its speed, accuracy, and repeatability. A machine vision system can easily assess object details too small to be seen by the human eye, and inspect them with greater reliability and lesser 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.
Optical Character recognition and visual inspection
Optical Character Recognition(left) and Defect Detection(right) are common aspects of machine vision in AVI
A traditional automated system, while minimising costs and improving efficiency does not have the flexibility or tolerance for variation that human beings do. Manual inspectors are able to distinguish between subtle, cosmetic and functional flaws, and can interpret 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 conceptualise and generalize. Humans excel at learning by example and can differentiate what really matters when it comes to slight anomalies between parts. This begs the question of how machine vision can make the best choice, in many cases, for the qualitative interpretation of a complex, unstructured scene — especially those with subtle defects and unpredictable flaws.
Why Machine Vision and Deep learning go hand-in-hand for this scenario
Although machine vision systems tolerate some variation in a part’s appearance due to scaling, rotation, and pose distortion, complex surface textures and image quality issues introduce serious inspection challenges. Machine vision systems alone fail to assess the vast possibility of variation and deviation between very visually similar images.