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MFFNet: Multi-modal Feature Fusion Network for Instance Segmentation of Froth Flotation Images
Ist Teil von
2023 2nd International Conference on Artificial Intelligence, Human-Computer Interaction and Robotics (AIHCIR), 2023, p.430-434
Ort / Verlag
IEEE
Erscheinungsjahr
2023
Quelle
IEEE/IET Electronic Library (IEL)
Beschreibungen/Notizen
In the field of ore resource processing, froth flotation is widely used to separate minerals. The common methods for assessing flotation status heavily rely on on-site observations by workers, which makes it difficult to ensure long-term stability. Traditional machine learning methods have limited accuracy in froth image segmentation, and the performance of semantic segmentation methods is restricted by inter-class imbalances caused by semantic labels. The extremely dense distribution in froth images also makes it in-sufficient to distinguish foreground from background using only the single modal. So, we design a four-camera system to generate multi-modal data (images and point clouds). The data are labeled at instance-level to create a FF4000 dataset. Based on instance segmentation, we propose a multi-modal feature fusion network named MFFNet, and experimental results demonstrate its effectiveness on the FF4000 dataset.