# Applications of Machine Learning and Computer Vision in Industry 4.0
## 2 Industry 4.0
### Machine and Deep Learning in Machine Vision Applications
Deep learning essentially teaches machines to do what comes naturally to humans: learn from examples. New inexpensive hardware has enabled the deployment of biologically inspired multi-layered \"deep\" neural networks that mimic the neural networks in the human brain. This gives manufacturing technology amazing new capabilities to recognize images, discern trends, and make intelligent predictions and decisions. Starting with the basic logic developed during initial training, deep neural networks can continuously improve their performance as they are exposed to new data, such as images or text [16]. Deep learning-based image analysis combines the specificity and flexibility of human visual inspection with the reliability, consistency, and speed of a computer system. Deep learning models can repeatedly and iteratively solve challenging computer vision applications that would be difficult to develop programmatically and that are often impossible to solve using traditional machine vision approaches. Deep learning models can distinguish unacceptable errors while tolerating natural variations in complex patterns. Moreover, they can be easily adapted to new examples without reprogramming their underlying algorithms [17]. Deep learning-based software can perform part localization, part inspection, part classification, and pattern recognition according to more effective judgments than humans or traditional machine vision solutions. The days when humans directly controlled production lines are long gone. Today, machines automate production, assembly and handling tasks. Machine vision systems equipped with precise setup and identification algorithms and guidance capabilities have made it possible to produce compact modern components that could not be assembled manually. On the production line, machine vision systems can reliably and repeatedly inspect hundreds or thousands of parts per minute, far exceeding the inspection capabilities of humans [18]. For decades, machine vision systems have taught computers to perform inspections that detect defects, contaminants, functional deficiencies, and other irregularities in manufactured products. Machine vision excels at quantitatively measuring a structured scene because of its speed, accuracy, and repeatability. A machine vision system built on the right resolution and camera optics can easily inspect the details of objects too small to be seen by the human eye and inspect them with greater reliability and less error [19]. Deep learning-based visual systems are the opposite of human visual inspection [18]:  
* More consistent--they work 24 \(\times\) 7 and maintain the same level of quality on every line, every shift, and every factory;
* More reliable--they identify every defect outside a set tolerance;
* Faster--identify defects in milliseconds, support high-speed applications and increase production throughput. Deep learning-based visual systems are in contrast to conventional machine vision systems [18]:  
* Designed for hard-to-solve applications--solves complex inspection, classification, and localization applications using classical rule-based algorithms;
* Easier to configure--applications can be set up quickly, speeding up proof of concept and development;
* Tolerates variation--handles error tolerances for applications that require evaluation of acceptable deviations from control. Traditional machine vision systems work reliably with consistent, well-made parts. They work through sequential filtering and rule-based algorithms that are more cost-effective than human inspection. But algorithms become impractical as exceptions and defect databases grow. Certain traditional machine vision inspections, such as final assembly verification, are notoriously difficult to program due to multiple variables that can be difficult for a machine to isolate, such as illumination or color changes [16].