An industrial 3D vision system for size measurement of iron ore green pellets using morphological image segmentation

An industrial prototype 3D imaging and analysis system has been developed that measures the pellet sieve size distribution into nine sieve size classes between 5 and 16+ mm. The system is installed and operational at a pellet production plant capturing and analysing 3D surface data of piled pellets on the conveyor belt. It provides fast, frequent, non-contact, consistent measurement of the pellet sieve size distribution and opens the door to autonomous closed loop control of the pellet balling disk or drum in the future. Segmentation methods based on mathematical morphology are applied to the 3D surface data to identify individual pellets. Determination of the entirely visible pellets is made using a new two feature classification, the advantage being that this system eliminates the resulting bias due to sizing partially visible (overlapped) particles based on their limited visible profile. The literature review highlights that in the area of size measurement of pellets and rocks, no other researchers make this distinction between entirely and partially visible particles. Sizing is performed based on best-fit-rectangle, classified into size classes based on one quarter of the measured sieving samples, and then compared against the remaining sieve samples.