Motion-based material characterization in sensor-based sorting

Maier, Georg; Pfaff, Florian; Becker, Florian; Pieper, Christoph; Gruna, Robin; Noack, Benjamin; Kruggel‐Emden, Harald; Längle, Thomas; Hanebeck, Uwe D.; Wirtz, S.; Scherer, Viktor; Beyerer, Jürgen

doi:10.1515/teme-2017-0063

Cited by 13 publications

(14 citation statements)

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“…In practice, other factors may impact the classification when measuring the position of the grains using an optical camera and a particle tracking algorithm [7,15]. They include a level of imprecision on the position and a limit to the frequency of image acquisition [16].…”

Section: Resultsmentioning

confidence: 99%

Classifying Grains Using Behaviour-Informed Machine Learning

Laudari

Marks

Rognon

2021

Preprint

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Sorting granular materials such as ores, coffee beans, cereals, gravels and pills is essential forapplications in mineral processing, agriculture and waste recycling. Existing sorting methods are based on the detection of contrast in grain properties including size, colour, density and chemical composition. However, many grain properties cannot be directly detected in-situ, which significantly impairs sorting efficacy. We show here that a simple neural network can infer contrast in a wide range of grain properties by detecting patterns in their observable kinematics. These properties include grain size, density, stiffness, friction, dissipation and adhesion. This method of classification based on behaviour can significantly widen the range of granular materials that can be sorted. It can similarly be applied to enhance the sorting of other particulate materials including cells and droplets in microfluidic devices.

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Section: Resultsmentioning

confidence: 99%

Classifying Grains Using Behaviour-Informed Machine Learning

Laudari

Marks

Rognon

2021

Preprint

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“…Furthermore, our system not only provides information regarding where an object is moving but also how it is moving. We consider utilizing motion information for the identification of the materials in real sorting scenarios as proposed in [39], [40] to be a promising research direction.…”

Section: Discussionmentioning

confidence: 99%

“…In ecoinformatics, motion features can be used to classify certain species, for instance birds [38]. In [39], [40], tracking has also been proposed to utilize motionbased features for the characterization of materials in sensorbased sorting. This approach can enable the discrimination of optically identical products, although an optical sensor is used.…”

Section: Related Workmentioning

confidence: 99%

Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking

Maier

Pfaff

Pieper

et al. 2021

IEEE Trans. Ind. Electron.

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Sensor-based sorting is a machine vision application that has found industrial application in various fields. An accept-or-reject task is executed by separating a material stream into two fractions. Current systems use line-scanning sensors, which is convenient as the material is perceived during transportation. However, line-scanning sensors yield a single observation of each object and no information about their movement. Due to a delay between localization and separation, assumptions regarding the location and point in time for separation need to be made based on the prior localization. Hence, it is necessary to ensure that all objects are transported at uniform velocities. This is often a complex and costly solution. In this paper, we propose a new method for reliably separating particles at non-uniform velocities. The problem is transferred from a mechanical to an algorithmic level. Our novel advanced image processing approach includes equipping the sorter with an area-scan camera in combination with a real-time multiobject tracking system, which enables predictions of the location of individual objects for separation. For the experimental validation of our approach, we present a modular sorting system, which allows comparing sorting results using a line-scan and area-scan camera. Results show that our approach performs reliable separation and hence increases sorting efficiency. Index Terms-Automated visual inspection, machine vision, real-time multiobject tracking, sensor-based sorting.

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“…There are also advanced algorithms for tracking and simulating single particle excretion. These algorithms are able to track objects on the conveyor belt and separate them from other objects with great precision [33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Sorting Systems for Transparent Plastic Granulate

Peršak

Viltužnik²,

Hernavs

et al. 2020

Applied Sciences

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Granulate material sorting is a mature and well-developed topic, due to its presence in various fields, such as the recycling, mining, and food industries. However, sorting can be improved, and artificial intelligence has been used for this purpose. This paper presents the development of an efficient sorting system for transparent polycarbonate plastic granulate, based on machine vision and air separation technology. The developed belt-type system is composed of a transparent conveyor with an integrated vision camera to detect defects in passing granulates. The vision system incorporates an industrial camera and backlight illumination. Individual particle localization and classification with the k-Nearest Neighbors algorithm were performed to determine the positions and conditions of each particle. Particles with defects are further separated pneumatically as they fall from the conveyor belt. Furthermore, an experiment was conducted whereby the combined performances of our sorting machine and classification method were evaluated. The results show that the developed system exhibits promising separation capabilities, despite numerous challenges accompanying the transparent granulate material.

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Motion-based material characterization in sensor-based sorting

Cited by 13 publications

References 0 publications

Classifying Grains Using Behaviour-Informed Machine Learning

Classifying Grains Using Behaviour-Informed Machine Learning

Experimental Evaluation of a Novel Sensor-Based Sorting Approach Featuring Predictive Real-Time Multiobject Tracking

Vision-Based Sorting Systems for Transparent Plastic Granulate

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