The Integration of Image Intensity and Texture for the Estimation of Particle Mass in Sorting Processes

Although mass is one of the most relevant process variables, industries may lack an inline monitoring of mass, which has a high cost in some cases. Due to their availability in sorting processes, cameras have potential as a low-cost alternative for the estimation of mass in recycling applications. Nevertheless, further research is needed to transform image information into mass. This work tackles this challenge by proposing a novel method of converting image information into mass of particles, complementing size measures with intensity and texture features extracted from the whole picture. Models were adjusted, employing machine learning techniques, using an industrial waste sample of post-consumer plastic film. The visual properties showed a dependency on mass labels, and the models achieved an error of 9 g for subsamples between 2 and 82 g. The analysis and validation of this image processing method provide a new alternative for the estimation of particle mass.