The visual characteristics of froths on flotation cell surfaces are crucial for the optimal control of the flotation process. However, previous research has primarily focused on optimizing markers to prevent the over-segmentation of froth images, often overlooking computational efficiency. This work proposes a deep learning-based froth segmentation algorithm that prioritizes both segmentation speed and quality. We propose a dataset construction strategy based on pseudo-labeling, which utilizes traditional methods to extract marker maps from froth images to build the dataset for deep learning. Subsequently, a semantic segmentation model is trained to derive marker maps from raw froth images. These marker maps are then employed in a marker-based watershed algorithm, enabling end-to-end segmentation of froth images. The proposed method is validated using froth images from a gold and nickel flotation plant, demonstrating the ability to accurately and efficiently identify different bubbles in real froth images without requiring complex pre-processing or post-processing steps. Industrial experiments show a tenfold increase in segmentation efficiency with virtually no loss of accuracy.