Image segmentation of adhesive ores based on MSBA-Unet and convex-hull defect detection

Abstract

Ore particle size information is a crucial indicator to evaluate the crushing quality and judge whether there are oversized ores on the conveyor belt. Accurately separating each ore is a critical prerequisite for obtaining high-precision particle size measurement (PSM) results. However, the large size variance and natural adhesion between ores pose a huge challenge to this task, imposing under-segmentation. Hence, this study proposes an automatic method that combines semantic segmentation and morphological operations to measure the ore particle size. Specifically, a novel multi-scale connection and boundary-aware U-Net model (MSBA-Unet) that classifies boundary pixels between adhesive ores more accurately is developed to segment ore images. Second, the convex-hull defect detection (CDD) method that divides the adhesive ores with a deep concave shape into two pieces is adopted to process the predicted masks further. The experimental results demonstrate that the MSBA-Unet architecture design and the CDD method can significantly improve the performance of separating adhesive ores of different sizes. Therefore, the under-segmentation problem is tremendously alleviated, and the ore PSM results agree well with the ground truth.