FEATURE Machine Vision
New optical sensors detect lumpy, bumpy or shiny objects
Where a sensing application involves the challenges of shiny, irregular, refl ective or ultra-black objects, such as car body parts or wafers with anti-refl ective coatings, the issues of poor accuracy and reliability become more signifi cant. These challenges often occur in the automotive and semiconductor industries but others, too. Now, sensing and instrumentation specialists Baumer has developed its new OT300 and OT500 optical sensors, which off er a wide detection range thanks to a line or spot beam time-of-fl ight measurement, independent of the angle
of installation. The sensors will reliably detect objects, even with a demanding and irregular surfaces. The sensors reliably detect objects at distances to 2.6m (OT500) and to 1.8m for the more-compact OT300, which is ideal for systems where space is very limited. The benefi ts of a compact design is supported by 3D CAD data with integrated beam path which is aligned to the mounting holes with a component tolerance compensation (qTarget), plus convenient teaching via qTeach and the standardised IO-Link interface with Smart Sensor Profi le (DMSS).
The combination of all these features allows users to achieve intuitive parameterisation by using the Baumer Sensor Suite software to improve process transparency thanks to easy access to secondary sensor data.
MVTec releases machine vision software based on deep learning
MVTec Software, the provider of machine- vision solutions, has released the 22.05 version of its HALCON machine- vision software. HALCON 22.05 has an expansion of the anomaly-detection technology, which improves deep- learning-based fault detection. It also includes new features and improvements to HALCON’s core technologies. As a result, the software now enables the practical implementation of software solutions for even more demanding applications across a wide range of industries. Companies using this machine- vision software benefi t from more effi cient production, especially in application areas like quality assurance. “The new technology provides our customers with brand-new possibilities, such as for inspection activities. We’ve also added a deep-learning-based training
option to the Deep OCR feature,” said Mario Bohnacker, Technical Product Manager for HALCON at MVTec Software.
By detecting logical anomalies in images, HALCON 22.05 can expand into new application areas. Until now, it has only been possible to detect structural anomalies strictly on a local level. The new Global Context Anomaly Detection feature is currently the only technology that can “understand” the logical content of the entire image. Like the existing
anomaly detection in HALCON, Global Context Anomaly Detection requires only “good images” for training. The training data does not need to be labelled. The technology can thus detect completely new anomaly variants, such as missing, deformed or incorrectly-arranged components on an assembly, for example. This opens up possibilities in new areas, such as the inspection of printed circuit boards in semiconductor manufacturing or printing verifi cation, among many others.
Hyperspectral imaging for machine vision
Machine vision is increasingly important for many applications, such as object classifi cation. However, relying on conventional RGB imaging is sometimes insuffi cient – the input images are just too similar, regardless of algorithmic sophistication.
Hyperspectral imaging adds the extra dimension of wavelength to conventional images, providing a much richer data set. Rather than expressing an image using red, green and blue (RGB) values at each pixel location, hyperspectral cameras instead record a complete spectrum at each point
34 May 2022 | Automation
to create a 3D data set, sometimes referred to as a hyperspectral data cube. The additional spectral dimension facilitates supervised learning algorithms that can characterise visually indistinguishable objects – highly desirable capabilities. Combining hyperspectral imaging with sensors that detect light beyond 1000nm enables even more information to be obtained, further enhancing the ability to distinguish diff erent materials. In-line inspection, in which a line- scan hyperspectral camera records items passing beneath it on a conveyor belt, is a
common installation approach. The camera speed can be adjusted and synchronised with the speed of the production line, ensuring that inspection covers 100% of the items in real time. The processed data output, such as a sorting result, can be integrated into existing machine-vision systems analogously to incumbent line-scan techniques. Appications include identifying, sorting and distinguishing objects, and even determining the thickness of thin fi lms from optical interference patterns.
automationmagazine.co.uk
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