FEATURE Machine vision


Determining a workpiece’s 3D form from a 2D image is fraught with challenges


by a human programmer. These accept inputs such as image pixel values, to yield outputs like 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. Therefore, they function as black boxes. Most such algorithms now employ deep learning based on artificial neural networks to perform 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, it can’t execute advanced pattern recognition required to identify individuals by


their faces, especially not from a video feed of a crowded space. In contrast, machine learning deftly handles such applications. No wonder then that machine learning is increasingly being applied to lower-level machine- vision operations, including image enhancement, restoration and feature detection.


Improving teaching approaches The maturing of deep-learning technology shows that it is not the algorithms’ learning that needs improving but their training. 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 –


Image sensors from the iVu series can identify workpieces by type, size, location, orientation and colouring. The machine vision components can accept configuration and monitoring from an integrated screen, remote HMI, or a PC. Camera, controller, lens and light are all pre-integrated


and then stores resultant information extracted 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. An old story about the early days of digital pattern recognition serves as a cautionary tale. The US military intended to use machine vision for target recognition: a defence contractor reliably identified US- and Russian- made tanks. Various tanks were all consistently correctly differentiated from the supplier’s aerial photographs. But, when tested again with the Pentagon’s own library of images, the system kept giving wrong answers. The problem was that the defence contractor’s images all picked US tanks when shown in desert environments and Russian tanks when shown in green fields. Far from recognising the right tank, the system was instead recognising different-coloured backgrounds. This stronly shows that, to be useful, learning algorithms must be trained carefully with proper training data.


Vision for robotic workcell safety Machine vision is no longer a niche technology, seeing massive adoption in industrial applications. Here, the most dramatic development is how machine vision now complements a factory’s safety systems, to sound alarms or audio announcements when personnel enter a working zone without safety gear on, or, for example, 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 during equipment operation – these and other systems based on machine vision will become a much more common part of factory processes.


18 May 2024 | Automation automationmagazine.co.uk


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