A Novel Particle Size Detection System Based on RGB-Laser Fusion Segmentation With Feature Dual-Recalibration for Blast Furnace Materials

Particle size detection (PSD) is used to obtain the particle size distribution of materials in the blast furnace charging process, which is significant for optimizing the gas flow distribution and ensuring stable production. However, due to the complex surface texture of the materials and the uneven illumination of the production environment, existing methods have difficulty obtaining the particle size distribution efficiently. This paper proposes an end-to-end PSD system based on image segmentation to obtain the particle size distribution online with high accuracy. First, to further enhance the expression of edge features and reduce the interference of complex textures, an RGB-laser particle segmentation network (RLPNet) is developed to obtain high-precision segmentation images by camera-LiDAR sensor fusion. Moreover, to improve the fusion of RGB and laser features, a feature dual-recalibration (FDR) module was designed and embedded in RLPNet, consisting of independent recalibration and joint recalibration with T-convolution. Finally, to reduce the error caused by missing edge particle pixels, an edge-recognition-based particle size calculation strategy (ERP) is presented. Experimental results demonstrate that the proposed method performs well on the constructed dataset and in industrial applications. With the segmentation accuracy of RLPNet reaching 64.19 $\%$ , the similarity between the particle size distribution predicted with ERP and the actual distribution reaches 79.19 $\%$ .