Detection of Coal and Gangue Based on Improved YOLOv8. [PDF]
Zeng Q +5 more
europepmc +1 more source
YOLOv8-Coal: a coal-rock image recognition method based on improved YOLOv8. [PDF]
Wang W, Zhao Y, Xue Z.
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BDL: transformer-based super-resolution network for degraded underground coal mine images. [PDF]
Hu T, Qiu J, Cheng X.
europepmc +1 more source
Research on improved gangue target detection algorithm based on Yolov8s. [PDF]
Fu Z, Yuan X, Xie Z, Li R, Huang L.
europepmc +1 more source
MCF-Net: Personnel and machinery detection model for complex downhole drilling environments. [PDF]
Jin Z, Li H.
europepmc +1 more source
Intelligent Recognition of Muffled Blasting Sounds and Lithology Prediction in Coal Mines Based on RDGNet. [PDF]
Li G, Ding H, Wang K, Zhang X, Sun J.
europepmc +1 more source
Top coal drawing law for an extra thick coal seam under the single round group drawing method. [PDF]
Pan W, Zhao Z, Li X, Xu Y, Zhang K.
europepmc +1 more source
Enhancing Object Detection in Underground Mines: UCM-Net and Self-Supervised Pre-Training. [PDF]
Zhou F +6 more
europepmc +1 more source
Measurement of sulfur content in coal mining areas by using field-remote sensing data and an integrated deep learning model. [PDF]
Liu J, Le BT.
europepmc +1 more source
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Abstract Advancing of the intelligent fully mechanized caving mining technology (IFMCMT) is the fundamental method to improve the output of coal resources. Accurate identification of coal gangue is the necessary condition for the realization of IFMCMT.
Yang Yang
exaly +2 more sources

