Details

Autor(en) / Beteiligte

• Dong, Wenkai
• Liu, Leiming
• Chen, Xiaoqi
• Ma, Jianfu
• Zhang, Tianxiang
• Li, Jiangyun

Titel

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.