Iván Hernández Dalas: How machine vision is enhancing automation safety and efficiency
Machine vision gives systems a high-level understanding of an environmental settings from images. | Source: Digikey
Machine vision is a collection of technologies that give automated equipment a high-level understanding of the immediate environment from images. Without machine vision software, digital images would be nothing more than simple, unconnected pixel collections.
Machine vision allows computers to detect edges and shapes within images, enabling higher-level processing routines to identify predefined objects of interest. Images, in this sense, are not necessarily limited to photographic images in the visible spectrum; they can also include images obtained using infrared, laser, X-ray, and ultrasound signals.
Today, one common machine vision application in industrial settings is to identify a specific part in a bin containing a randomly arranged mix of parts. Here, machine vision can help pick-and-place robots automatically pick up the right part.
Of course, recognizing such parts with imaging feedback would be relatively straightforward if they were all neatly arranged and oriented the same way on a tray. However, robust machine vision algorithms can recognize objects at different distances from the camera as well as in different orientations.
The most sophisticated machine vision systems have enabled new and emerging designs far more sophisticated than bin picking.
Machine vision technologies
3D scanners capture 2D images of an object to create a 3D model of it. In some cases, digital models are then employed to 3D print copies. | Source: Digikey
The term machine vision is sometimes reserved to reference more established and efficient mathematical methods of extracting information from images.
In contrast, the term computer vision typically describes more modern and computationally demanding systems — including black-box approaches using machine learning or artificial intelligence (AI).
However, machine vision can also serve as a catch-all term, encompassing all methods of high-level information extraction from images.
Technologies to extract high-level meaning from images are increasingly common. Within the research community, such technologies are often considered different from machine vision. However, in a practical sense, all are different ways of achieving machine vision, and in many cases, overlap.
Digital image processing is a form of digital signal processing involving image enhancement, restoration, encoding, and compression. Advantages over analog image processing include minimized noise and distortion, as well as the availability of far more algorithms.
Digital image enhancement often involves increasing contrast and may also make geometric corrections for viewing angle and lens distortion. Compression is typically achieved by approximating a complex signal to a combination of cosine functions.
Photogrammetry employs some kind of feature identification to extract measurements from images. These measurements can include 3D information when multiple images of the same scene have been obtained from different positions.
The simplest photogrammetry systems measure the distance between two points in an image employing a scale. It is normally required to include a known scale reference in the image for this purpose.
Feature detection lets computers identify edges and corners, or points in an image. This is a required first step for photogrammetry, as well as the identification of objects and motion. Blob detection can identify regions with edges that are too smooth for edge or corner detection.
Pattern recognition is used to identify specific objects. At its simplest, this might mean looking for a specific, well-defined mechanical part on a conveyor.
3D reconstruction determines the 3D form of objects from 2D images. It can be achieved by photogrammetric methods in which the height of common features (identified in images from different observation points) is determined by triangulation. 3D reconstruction is also possible using a single 2D image.
How machine vision tasks are ordered
Many machine vision systems progressively combine the above techniques by starting with low-level operations and then advancing one by one to higher-level operations. At the lowest level, all of an image’s pixels are held as high-bandwidth data. Then, each operation in the sequence identifies image features and represents information of interest with relatively small amounts of data.
The low-level operations of image enhancement and restoration come first, followed by feature detection. Where multiple sensors are used, low-level operations may therefore be carried out by distributed processes dedicated to individual sensors. Once features in individual images are detected, higher-level photogrammetric measurements can occur, as can any object identification or other tasks relying on the combined data from multiple images and sensors.
Direct computations and learning algorithms
A direct computation in the context of machine vision is a set of mathematical functions that are manually defined by a human programmer. These accept inputs such as image pixel values to yield outputs such as the coordinates of an object’s edges.
In contrast, learning algorithms aren’t directly written by humans but are instead trained via example datasets associating inputs with desired outputs. Functioning like black boxes, machine learning now employs deep learning based on artificial neural networks to make its calculations.
Simple machine learning for industrial applications is often more reliable and less computationally demanding if based on direct computation. Of course, there are limits to what can be achieved with direct computation.
For example, direct computation could not execute the advanced pattern recognition required to identify individuals by their faces, especially from a video feed of a crowded public space. In contrast, machine learning deftly handles such applications. No wonder then that machine learning is increasingly being deployed for lower-level machine vision operations, including image enhancement, restoration, and feature detection.
Machine vision for industrial safety
Image sensors can identify workpieces by type, size, location, orientation, and coloring. | Source: Digikey
Machine vision is no longer a niche technology. The technology is experiencing the greatest deployment growth within industrial applications. The most dramatic development is how machine vision now complements industrial-plant safety systems that sound alarms or issue audio announcements when plant personnel enter a working zone without a hard hat, mask, or other correct protective equipment. Machine vision can also complete systems that announce when mobile machinery, such as forklifts, gets too close to people.
These and similar machine vision systems can sometimes replace hard guarding around industrial robots to enable more efficient operations. They can also replace or enhance safety systems based on light guards that simply stop machinery if a plant worker enters a workcell. When machine vision monitors the factory floor surrounding the workcell, it is possible for robots in such cells to gradually slow down as people approach.
The designs of industrial settings are evolving to accommodate collaborative robots and other workcell equipment that are safe for plant personnel to move around, even while that equipment operates. These and other systems based on machine vision will become a much more common part of factory processes.
By understanding how to design and deploy smart machine vision systems, engineers and manufacturers can smartly integrate visual intelligence tools on the factory floor, enhancing safety and efficiency. As technology evolves at a rapid pace, DigiKey continues to offer innovative automation solutions from IoT to AI and machine vision.
About the Author
Rich Miron is a senior technical content developer for DigiKey. DigiKey is recognized as the global leader and continuous innovator in the cutting-edge commerce distribution of electronic components and automation products worldwide, providing more than 16.5 million components from over 3,000 quality name-brand manufacturers.
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