Real-time hyperspectral processing for automatic nonferrous material sorting

Abstract

Abstract. The application of hyperspectral sensors in the development of machine vision solutions has become increasingly popular as the spectral characteristics of the imaged materials are better modeled in the hyperspectral domain than in the standard trichromatic red, green, blue data. While there is no doubt that the availability of detailed spectral information is opportune as it opens the possibility to construct robust image descriptors, it also raises a substantial challenge when this high-dimensional data is used in the development of real-time machine vision systems. To alleviate the computational demand, often decorrelation techniques are commonly applied prior to feature extraction. While this approach has reduced to some extent the size of the spectral descriptor, data decorrelation alone proved insufficient in attaining real-time classification. This fact is particularly apparent when pixel-wise image descriptors are not sufficiently robust to model the spectral characteristics of the imaged materials, a case when the spatial information (or textural properties) also has to be included in the classification process. The integration of spectral and spatial information entails a substantial computational cost, and as a result the prospects of real-time operation for the developed machine vision system are compromised. To answer this requirement, in this paper we have reengineered the approach behind the integration of the spectral and spatial information in the material classification process to allow the real-time sorting of the nonferrous fractions that are contained in the waste of electric and electronic equipment scrap.

Highly Evolvable E-waste Recycling Technologies and Systems

“…Although hyperspectral technologies can help characterizing the composition of metallic materials, current hyperspectral processing approaches involve poor characterization of the inherent variability of these materials and their visual appearance (optical texture, geometry, and pattern) of the particles [23]. Besides this, the high amount of data that is inherent to hyperspectral imaging implies slow data processing rate, which is not acceptable for real-time recycling applications [24][25][26]. Thus, new real-time hyperspectral models enclosing texture, optical pattern, component variability and adaptability to improve the overall recycling process are necessary.…”

Design, management and control of demanufacturing and remanufacturing systems

“…HyperSpectral Imaging (HSI) technologies have been proposed as promising solutions. Also known as Spectroscopic Imaging and originally developed for mining and geology, HSI has now spread into different fields, such as pharmaceutical and food sectors, and is penetrating the recycling market, especially for the characterisation in polyolefin packaging waste [274], construction and demolition waste, and waste from electric and electronic equipment (WEEE) [218,219]. Imaging spectroscopy allows the simultaneous determination of the optical spectrum components and the spatial location of an object in a surface.…”

Characterization of Fine Metal Particles Derived from Shredded WEEE Using a Hyperspectral Image System: Preliminary Results

“…The majority of contributions is related to the cooperative research project SORMEN [ 5 ], a project devoted to the development of a new technology based on multispectral and hyperspectral characterization of WEEE mixtures for the separation of non-ferrous metal particles. In particular, [ 6 , 7 , 8 , 9 ] describe the main outcome of the project: a framework for the automatic sorting of non-ferrous materials exploiting hyperspectral data. The authors report the results obtained testing a new technique for the decorrelation of hyperspectral data and a classification algorithm, which integrates the spectral and spatial features of particles.…”