Real-time Segmentation Model of Underground Coal Flow Based on CFU-Net
ZHANG Fei
ZHANG Mei
Abstract：To address the difficulty in coal flow detection caused by strong dust interference and complex illumination in underground coal mines,a real-time segmentation model of underground coal flow based on the coal flow U-shaped network(CFU-Net)was developed.Firstly,the backbone network structure of U-Net was improved based on the lightweight mobile network version 4(MobileNetV4),and the operational efficiency of the model was thereby enhanced.Secondly,a lightweight multi-scale feature preservation(LMFP)module and a dynamic sampling(DySample)operator were employed to compensate for feature loss caused by channel compression and to strengthen the model's detail reconstruction capability.Finally,a dynamic feature interaction(DFI)module was introduced to refine the skip connections of U-Net.Through this module,adaptive deep fusion of cross-layer features was realized,and the model's perception capability for features at different scales was enhanced.The results showed that the mean intersection over union(mIoU)of the CFU-Net model was 95.10%,with the detection speed increased to 70.20 frames/s,thus possessing the advantages of high accuracy and high speed.When compared with mainstream models such as the pyramid scene parsing network(PSPNet)and the deep dual-resolution network(DDRNet),significant advantages were demonstrated.The research confirmed that real-time segmentation model of underground coal flow based on CFU-Net could satisfy coal flow detection requirements in real situations.
Keywords：coal flow detectionbelt conveyorsemantic segmentationimproved U-Netmulti-scale fusion
Publication Date：2025-09-20
Online Publishing Date：2025-09-24(First online date of this platform, not the publication date of the document)
Pages：6( 382-387 )