Mask R-CNN Applied to Quasi-particle Segmentation from the Hybrid Pelletized Sinter (HPS) Process

Particle size is an important quality parameter for raw materials in steel industry processes. In this work, we propose to implement the Mask-R-CNN algorithm to segment quasi-particles by size classes. We created a dataset with real images of an industrial environment, labeled the quasi-particles by size classes, and performed four training sessions by adjusting the model's hyperparameters. The results indicated that the model segments with well-defined edges and applications as classes correctly. We obtained a mAP between 0.2333 and 0.2585. Additionally. hit and detection rates increase for larger particle size classes.