Simulation study and experimental validation of a neural network-based predictive tracking system for sensor-based sorting

Abstract

Sensor-based sorting offers cutting-edge solutions for separating granular materials. The line-scanning sensors currently in use in such systems only produce a single observation of each object and no data on its movement. According to recent studies, using an area-scan camera has the potential to reduce both characterization and separation error in a sorting process. A predictive tracking approach based on Kalman filters makes it possible to estimate the followed paths and parametrize a unique motion model for each object using a multiobject tracking system. While earlier studies concentrated on physically-motivated motion models, it has been demonstrated that novel machine learning techniques produce predictions that are more accurate. In this paper, we describe the creation of a predictive tracking system based on neural networks. The new algorithm is applied to an experimental sorting system and to a numerical model of the sorter. Although the new approach does not yet fully reach the achieved sorting quality of the existing approaches, it allows the use of the general method without requiring expert knowledge or a fundamental understanding of the parameterization of the particle motion model.

An Image‐Based Approach to Automated Recognition of Asbestos‐Containing Components in Wall Demolition Waste

“…More recently, a further developed approach has been applied to a laboratory-scale optical sorter [37]. Here, recurrent neural networks have been used to track and predict the movement of particles on the conveyor belt based on image data.…”

Optimizing Multi-Spectral Ore Sorting: Incorporating Wavelength Selection Utilizing Neighborhood Component Analysis for Effective Arsenic Mineral Detection

“…SBOS methods have utilized a range of sensing technologies, including X-ray transmission, X-ray fluorescence, optical sensing, and inductive sensing [9], [26], [27]. Furthermore, the utilization of area-scan cameras and predictive tracking systems that rely on machine learning approaches have demonstrated potential in minimizing characterization and separation errors [28]. Researchers have also studied the combination of data from several sensing approaches to improve the sorting ability of these systems [29].…”