# Applications of Machine Learning and Computer Vision in Industry 4.0
## 2 Industry 4.0
### Machine and Deep Learning in Machine Vision Applications  
The first optoelectronic CCD sensor was developed by Kodak in the 1970s. In parallel, CMOS sensors were also developed, initially as a cheaper option compared to CCDs with noticeably poorer image quality. CMOS technology is used for the production of most integrated circuits, such as semiconductor memories in computers. The disadvantages of CCDs have been gradually eliminated or suppressed due to development, and so CCD sensors are slowly going away [11]. However, development is constantly moving forward, so silicon-based semiconductor sensors as we know them today may be completely a thing of the past in a few years. Research is testing chemical elements and materials such as Germanium or Graphene, which show much better properties than silicon, such as several orders of magnitude higher sensitivity. The cost estimates of mass production also speak in favor of new types of sensors. Photosensitive sensors have applications in both consumer electronics and industrial digital cameras. For machine vision technology itself, the impact of developments in sensors is evident in the ability to better record the scene being imaged in the form of higher-quality digital images. Other hardware innovations include higher transmission speeds in the form of the 25 GigE camera introduced. However, devices supporting 10 GigE have been slow to appear so far. Another example is the Dual USB3 camera with two USB3.0 ports and twice the transfer rate [11]. Machine vision is thus evolving thanks to advances in computer vision and new technologies and products. What used to be difficult or nonexistent can nowadays be the standard. And how does this fit into the Industry 4.0 concept? Machine vision is capable of replacing humans because of its ability to see and also understand what is seen. When considering the sensory perception of objects, it is often visual perception that is reached for, as about 90-95% of the information is drawn through this sensory channel [12]. Thus, in today's information age, the replacement of humans or the integration of machine vision systems into the production process is quite logical and very common due to this fact. Current modern systems are able to adapt to rapid changes in production and allow a high degree of versatility. The application is mainly in the field of quality control, where the human factor very often fails or is not able to meet the productivity or accuracy requirements of the inspections performed [13]. Machine vision is an essential element of an automation system. No other aspect of the production line captures more information or is more valuable in assessing products and detecting defects, as well as gathering data to guide operations and optimize the productivity of robots and other equipment. Unlike simple sensors, vision sensors generate large amounts of image data, increasing their usefulness in Industry 4.0 environments [14]. As data analytics capabilities advance, the large volumes of data accessed through vision equipment will be used to identify and flag defective products, understand their shortcomings, and enable fast and effective intervention in an Industry 4.0 manufacturing plant [14]. A current trend in image processing is the use of GPUs (Graphics Processing Units). These are processing units that consist of thousands of relatively simple computing cores on a single chip. Their architecture looks like that of a neural network. This allows the use of biologically inspired and multi-layered \"deep\" neural networks resembling the human brain [15]. Deep learning has emerged as the core speech, text, and face recognition technology we use in our mobile and wearable devices and is now beginning to be used in many other applications--from medical diagnostics to internet security--to predict patterns and make critical business decisions. The same technology is now making its way into advanced manufacturing processes, quality control, and other decision-based uses [15].