Machine vision is a paradigm and an umbrella term that encompasses all industrial and non-industrial applications in which a combination of hardware and software provides operational guidance to devices in operations and functioning based on the capture and processing of images. Though industrial computer vision uses many of the same algorithms and approaches as academic and military applications of computer vision, the constraints are usually very different.
Industrial vision systems are required to be more robust, more reliable, and stable compared to an academic and research-oriented vision system. They typically also cost much less than those used in military applications. Therefore, industrial machine vision systems are usually low cost, of acceptable accuracy, are highly reliable and robust, and possess high mechanical and temperature stability.
Within the past few years, Machine Vision has gained massive popularity in dynamic industries such as retail and manufacturing. These industries are leveraging the technology to enhance their customer experience, optimize the usage of resources, and achieve better quality assurance.
Although machine vision is seeing growing applications alongside the advances in technology, there are a few major applications where machine vision has proven extremely valuable.
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Machine vision guidance has numerous useful applications in the manufacturing industry. For the most part, it involves locating a specified part and ensuring proper placement and positioning so that production runs seamlessly and with minimum errors and downtime. Machine vision techniques can also be used to specify the location and orientation of a particular part. This information can then be transmitted to a robot or machine controller for production purposes.
Machine vision guidance enables more efficient and more accurate production than manual positioning with employees, especially in assembly lines or arranging parts on pallets.
Machine vision techniques used in identification applications mostly involve reading barcodes and data matrix codes to identify and categorize various products. This is crucial for error-proofing production and packaging processes. Additionally, it is much faster and more accurate than manual error proofing. Machine vision identification can also be used for optimizing productivity by identifying bottlenecks in production pipelines.
Machine vision gauging is a technique that is used exclusively on production lines. In this application, a fixed-mount camera identifies two or more points on an object as it passes by on the production line. If a discrepancy between the distances measured and the distances programmed into the vision system is detected, the part is pushed off the line as it contains some form of a production error. Machine vision guidance provides greater speed and accuracy over traditional techniques such as contact gauging.
4. INSPECTION & FLAW DETECTION
Similar to gauging, machine vision inspection is principally used for the flaw and defect detection. Machine vision inspection and flaw detection offer greater flexibility to inspect a wide variety of objects in a large number of industry applications, including decay in agricultural products, flaws in textiles, branding marks in prescription tablets, and more. Inspection don using machine vision is much quicker and far more accurate than manual inspection processes.
5. OBJECT DETECTION
In object detection, the algorithm looks for individual objects rather than the entire image. Here the algorithm is essentially trying to determine objects present or absent in the image as opposed to classifying the entire image. Varieties of techniques are used to perform efficient object detection. Object detection algorithms can be applied at various points within the manufacturing chain such as quality management, inventory management, sorting, assembly line, etc.
A fantastic example of an application of this problem type is the engine assembly chain of a car manufacturer where machine vision can analyze the image for the engine and based on its previous training and experience correctly confirm the presence or absence of parts in that particular engine block.
6. PRINT DEFECT DETECTION
Usually, manual print quality inspection techniques inevitably fall short of what is required for capturing defects consistently. The shortcomings translate into lower revenues, reduced profitability, and deterioration in the quality of final products. Automated platforms such as a print defect detection using machine vision help companies prevent that from happening.
The technology has many applications and works very well for print, labels, and packaging inspection. Ultimately, the solution gets rid of the immense pressure placed on operators for quality assurance and takes on the task of ensuring automatic detection with impeccable accuracy.
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Classification involves predicting which class or category an item belongs to. Some classifiers output binary classifications like yes/no. Some are multi-class, that categorize items into one of several possible categories. Classification is a common use case of deep learning—classification algorithms are used to solve problem categorization, image recognition, and image-based classification in the industrial manufacturing environment. In classification problems, the input is usually an entire image. The algorithm processes the entire image and further classifies it based on its previous training. A great example of this could be the classification of objects in an assembly line.
Computer vision is enabling various industries such as retail, insurance, manufacturing, etc. achieve greater customer delight and satisfaction. Technological advances in the machine vision field are continuing to expand the capabilities of the across industrial sectors and applications. This development is manifesting itself as greater accuracy, repeatability, quality, and efficiencies in manufacturing.