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Image-Based Iron Slag Segmentation via Graph Convolutional Networks

Slagging-off (i.e., slag removal) is an important preprocessing operation of steel-making to improve the purity of iron. Current manual-operated slag removal schemes are inefficient and labor-intensive. Automatic slagging-off is desirable but challenging as the reliable recognition of iron and slag is difficult. This work focuses on realizing an efficient and accurate recognition algorithm of iron and slag, which is conducive to realize automatic slagging-off operation. Motivated by the recent success of deep learning techniques in smart manufacturing, we introduce deep learning methods to this field for the first time. The monotonous gray value of industry images, poor image quality, and nonrigid feature of iron and slag challenge the existing fully convolutional networks (FCNs). To this end, we propose a novel spatial and feature graph convolutional network (SFGCN) module. SFGCN module can be easily inserted in FCNs to improve the reasoning ability of global contextual information, which is helpful to enhance the segmentation accuracy of small objects and isolated areas. To verify the validity of the SFGCN module, we create an industrial dataset and conduct extensive experiments. Finally, the results show that our SFGCN module brings a consistent performance boost for a wide range of FCNs. Moreover, by adopting a lightweight network as backbone, our method achieves real-time iron and slag segmentation. In the future work, we will dedicate our efforts to the weakly supervised learning for quick annotation of big data stream to improve the generalization ability of current models.