Work

Optimized Image Segmentation Model for Pellet Microstructure Incorporating KL Divergence Constraints

Year: 2026

Type: article

Abstract: Accurate segmentation of pellet microstructure images is crucial for evaluating their metallurgical performance and optimizing production processes. To address the challenges posed by complex structures, blurred boundaries, and fine-grained textures of hematite and magnetite in pellet micrographs, this study proposes a hybrid intelligently optimized VGG16-U-Net semantic segmentation model. The model incorporates an improved SPC-SA channel self-attention mechanism in the encoder to enhance deep feature representation, while a simplified SAN and SAW module is integrated into the decoder to strengthen its response to key mineral regions. Additionally, a hybrid loss strategy is employed with KL regularization for training optimization. Experimental results show that the model achieves an mIoU of 85.58%, an mPA of 91.54%, and an overall accuracy of 93.58%. Compared with the baseline models, the proposed method achieves improved performance to some extent.

Source: Mathematics

DOI: 10.3390/math14030574

Authors: Yuwen Ai, Xia Li, Aimin Yang, Yunjie Bai, Xuezhi Wu

Institutions: North China University of Science and Technology, Hebei Normal University of Science and Technology

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Natural Science Foundation of China