How machine vision is advancing automation now
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) are determined by triangulation. 3D reconstruction is also possible using a single 2D image; here, software interprets (among other things) the geometric relationships between edges or regions of shading. A human can mentally reconstruct a cube from a simple line-art representation with ease – and a sphere from a shaded circle. Shading gives indication of the surfaces’ slopes. However, the process of such deduction is more complicated than it seems because shading is a one-dimensional parameter while slope occurs in two dimensions. This can lead to ambiguities – a fact demonstrated by art depicting physically impossible objects. 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.
An old story about the early days of digital pattern recognition serves as a cautionary tale. The U.S. military intended to use machine vision for target recognition, and defense- contractor demonstrations reliably identified U.S.-made and Russian- made tanks. Various tanks were all correctly differentiated from the supplier’s aerial photographs, one after the other. But when tested again with the Pentagon’s own library of pictures, the system kept giving wrong answers. The problem was that the defense contractor’s images all depicted U.S. tanks in deserts and Russian tanks in green fields. Far from recognizing different tanks, the system was instead recognizing different- colored backgrounds. The moral? Learning algorithms need to be presented with carefully curated training data to be useful.
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 get 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. As the designs of industrial settings evolve 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. Figure 6: Image sensors from the iVu series can identify workpieces by type, size, location, orientation, and coloring. The machine-vision components can accept configuration and monitoring an integrated screen, remote HMI, or PC. Camera, controller, lens, and light are all pre-integrated. Image source: Banner Engineering Corp.
Figure 5: Computerized
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 an object’s edges’ coordinates. In contrast, learning algorithms aren’t directly written by humans but are instead trained via example datasets associating inputs with desired outputs. They, therefore, function as black boxes. Most all such 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
determination of a workpiece’s 3D form from a 2D image is fraught with challenges.
direct computation. For example, it could never hope to execute the advanced pattern recognition required to identify individuals by their faces, especially not 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. Improving teaching approaches (not algorithms) The maturing of deep-learning technology has made apparent that it’s not learning algorithms themselves needing improvement but the way they’re trained. One such improved training routine is called data-centric computer vision. Here, the deep-learning system accepts very large training sets made of thousands, millions, or even billions of images – and then stores resultant information its algorithms extract from each image. The algorithms effectively learn by practicing worked examples and then referring to an ‘answer book’ to verify whether they arrived at the right values.
Conclusion: vision for robotic workcell safety
Machine vision is no longer a niche technology. It’s seeing the most increased deployment in industrial applications. Here, the most dramatic development is how machine vision now complements industrial-plant safety systems that sound alarms or issue audio
Figure 4: 3D scanners capture 2D images of an object to create a 3D model of it. In some cases, the digital models are then employed to 3D print copies. Image source: Shenzhen Creality 3D Technology Co.
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