Computer vision makes sight accessible to machines.
There are good reasons why sight is considered our most important sense: approximately a quarter of the human brain deals with perceiving and interpreting light. Computer vision makes this sense accessible to machines. Unlike human vision, however, computer vision processes image data from a tremendous variety of sources, such as document scanners, smartphones, film cameras, high-performance industrial cameras, and medical imaging technology. In doing so, computer sensors also process more than visible light. Instead, they also detect infrared radiation and microwaves, as well as X-ray waves and UV. They can also handle a wide spectrum of spatial resolutions, from a few micrometres per pixel in microscopy to several meters per pixel in satellite images. This incredible range is coupled with a no less impressive variety of processing methods, from classic image processing to deep learning. We’ll help you to keep things straight!
For inovex, computer vision serves, on the one hand, as the first section of a longer pipeline: as a data source for Natural Language Processing, for example, or to provide location awareness for augmented reality, self-driving vehicles, and controlling robots. On the other hand, computer vision is also the primary driver in self-contained applications, such as in quality control for industrial production, in sorting recycling streams, or in searching through large image and video archives. Typical application areas for computer vision include agriculture and industry and logistics and trade, as well as medical technology and remote sensing.
Computer Vision for Agriculture and Industry
As a tool for performing automatic optical inspections, computer vision enables fast, consistent, and complete quality assurance for production goods, whether at the inbound, intermediate, or final inspection stages. It enables production processes to be monitored around the clock to detect and correct potential errors in the early stages.
This technology is used in many industrial processes across a variety of industries. In circuit board assembly, for example, optical inspections verify component position and type and check soldering points for quality and completeness. The technology’s ability to automatically detect scratches, dents, cracks, and other defects enables it to be used to evaluate the surfaces of car bodies, for example, as well as sections of tile or fabric. Similar techniques are used in drug manufacturing to detect damaged tablets and incomplete or incorrectly filled tablet blisters. In the food industry, optical inspections monitor the growth of plants and assess the ripeness of fruit to determine the optimum harvesting point. They are also used immediately after harvesting to detect and sort out mouldy or rotten fruit. Beekeepers can use this technology to monitor the health and size of their colonies, while livestock breeders can detect diseased animals or problematic behaviour in individuals.
Many of these applications are simplified – or even made possible – by machine learning. This is especially true when state-of-the-art sensors, like multispectral and hyperspectral cameras, are used. These allow conclusions to be drawn about the chemical composition of materials, the details of which are imperceptible to humans.
Computer Vision for Logistics and Trade
Logistics and commerce also offer many starting points for computer vision. Shipping service providers have long relied on computerized address recognition to automate package distribution. Modern machine learning methods enable packing robots to assemble individual orders or to verify that a package is complete and filled with the right products. Similar algorithms allow regular automated inventories of large warehouses. These can be combined with predictions about future product demand made using classic data science to enable parts of the warehousing process to be automated.
Optical Character Recognition and content-based image searches make digitized catalogues searchable by and available to both market researchers and consumers. These can, for example, be used to locate suitable spare parts, or by the fashion industry to enable photos to be used in suggesting and virtually trying on garments.
Computer Vision for Remote Sensing
Computer vision also helps evaluate images taken from a great distance. It enables aerial photographs to be used to analyse visitor flows at major events in order to plan optimal traffic routes, or to identify and eliminate potential hazard points before accidents occur. This type of image data also allows monitoring of the condition of critical infrastructure, including railways, roads, waterways, and power lines. It also enables different types of ground cover, such as water, forest, grass, rock, or sealed areas, to be identified, allowing preparations to be made for construction projects. It can also be combined with multispectral imaging to monitor the health of agricultural land, additionally detecting changes in water quality or forest loss caused by illegal clearing.
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