Multiscale Neighborhood Adaptive Clustering Image Segmentation for Molten Iron Flow Slag-Iron Recognition

Abstract

Accurately measuring the slag-iron ratio (SIR) of the molten iron flow is of great significance for efficient slag-iron emission and safe production in the ironmaking process. Slag-iron recognition is challenging due to the high temperature, intense radiation, and heavy dust in the casting field. Efficient and intelligent online slag-iron recognition is urgently needed in ironmaking enterprises. To this end, this article innovatively presents a vision-driven slag-iron recognition method based on multiscale neighborhood adaptive clustering (MNAC) to achieve continuous high-precision SIR measurement of molten iron flow. First, a shortwave infrared high-speed imaging (SWIHI) system is designed and deployed to capture high-definition molten iron flow images. Then, a multialgorithm fusion key frame and region of interest (ROI) acquisition strategy is proposed to achieve stable acquisition of molten iron flow boundaries and prevent the ROI from being occluded by dust and objects. Finally, a novel multiscale neighborhood slag-iron recognition strategy and an adaptive density peak clustering algorithm are presented to achieve high-precision slag-iron recognition through the kernel density estimation of the membership of a single pixel in multiple neighborhoods. Industrial experiments and applications indicate that the proposed method can achieve slag-iron recognition continuously and accurately to provide reliable SIR data for blast furnace experts.