Bottom argon blowing in the ladle is a critical step in steelmaking, where the exposed surface area of molten steel (argon flower) serves as an important indicator for evaluating the blowing efficiency. To achieve quantitative analysis of the argon flower, image segmentation techniques are employed. However, existing segmentation networks generally suffer from large parameter sizes, high computational resource requirements, and limited segmentation accuracy, making them unable to meet the real-time and efficiency demands of industrial production. This paper proposes an innovative argon flower segmentation network named ArgusFusion. The network adopts a U-shaped architecture and integrates convolutional modules with a novel Global Multi-Layer Perceptron-based Attention (Glo-MLP attention) mechanism during feature extraction and reconstruction stages to facilitate efficient information exchange. In the bottleneck layer, an improved Multi-Scale Channel Attention Mixer (MACA-Mixer) is introduced to enhance feature representation. Additionally, a Adaptive Hierarchical Feature Fusion (AHFF) is incorporated into the skip connections to optimize boundary segmentation. Experimental results on an industrial argon flower dataset demonstrate that ArgusFusion achieves an IoU of 88.90% with only 0.51 M parameters and 1.38 GFLOPs, showcasing high segmentation accuracy and low computational cost, fully meeting the requirements of real-time industrial applications.