GC-FSegNet: A Flotation Froth Segmentation Network with Integrated Global Context Awareness

Precise segmentation of flotation froths is a critical bottleneck to achieving intelligent perception and optimal control of process operations. Traditional convolutional neural networks (CNNs) are inherently limited by local receptive fields, making it challenging to accurately segment adhesive and multi-scale froths. To address this fundamental issue, this paper proposes a deep segmentation network with integrated global context awareness, termed GC-FSegNet, which establishes a new paradigm capable of jointly modeling macro-level structures and micro-level details. The proposed GC-FSegNet innovatively integrates the Global Context Network (GCNet) module into both the encoder and decoder of a Nested U-Net architecture. The GCNet captures long-range dependencies between froths, enabling macro-level modeling of clustered foam structures, while the Nested U-Net preserves high-resolution boundary details. Through their synergistic interaction, the model achieves simultaneous and efficient representation of both global contours and local details of froth images. Furthermore, the Mish activation function is employed to enhance the learning of weak boundary features, and a combined Dice and Binary Cross-Entropy (BCE) loss function is designed to optimize boundary segmentation accuracy. Experimental results on a self-constructed copper–lead flotation froth dataset demonstrate that GC-FSegNet achieves an mDice of 0.9443, mIoU of 0.8945, mRecall of 0.9866, and mPrecision of 0.9705, significantly outperforming mainstream models such as U-Net and DeepLabV3+. This study not only provides a reliable technical solution for high-adhesion froth segmentation but, more importantly, introduces a promising “global–local collaborative modeling” framework that can be extended to a wide range of complex industrial image segmentation scenarios.