Rapid and accurate recognition of coal and rock is an important prerequisite for safe and efficient coal mining.In this paper,a novel coal-rock recognition method is proposed based on fusing laser point cloud and imag...Rapid and accurate recognition of coal and rock is an important prerequisite for safe and efficient coal mining.In this paper,a novel coal-rock recognition method is proposed based on fusing laser point cloud and images,named Multi-Modal Frustum PointNet(MMFP).Firstly,MobileNetV3 is used as the backbone network of Mask R-CNN to reduce the network parameters and compress the model volume.The dilated convolutional block attention mechanism(Dilated CBAM)and inception structure are combined with MobileNetV3 to further enhance the detection accuracy.Subsequently,the 2D target candidate box is calculated through the improved Mask R-CNN,and the frustum point cloud in the 2D target candidate box is extracted to reduce the calculation scale and spatial search range.Then,the self-attention PointNet is constructed to segment the fused point cloud within the frustum range,and the bounding box regression network is used to predict the bounding box parameters.Finally,an experimental platform of shearer coal wall cutting is established,and multiple comparative experiments are conducted.Experimental results indicate that the proposed coal-rock recognition method is superior to other advanced models.展开更多
3D object detection is a critical technology in many applications,and among the various detection methods,pointcloud-based methods have been the most popular research topic in recent years.Since Graph Neural Network(G...3D object detection is a critical technology in many applications,and among the various detection methods,pointcloud-based methods have been the most popular research topic in recent years.Since Graph Neural Network(GNN)is considered to be effective in dealing with pointclouds,in this work,we combined it with the attention mechanism and proposed a 3D object detection method named PointGAT.Our proposed PointGAT outperforms previous approaches on the KITTI test dataset.Experiments in real campus scenarios also demonstrate the potential of our method for further applications.展开更多
基金supported in part by the National Natural Science Foundation of China(Nos.52174152 and 52074271)in part by the Xuzhou Basic Research Program Project(No.KC23051)+2 种基金in part by the Shandong Province Technology Innovation Guidance Plan(Central Guidance for Local Scientific and Technological Development Fund)(No.YDZX2024119)in part by the Graduate Innovation Program of China University of Mining and Technology(No.2025WLKXJ088)in part by the Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX252830).
文摘Rapid and accurate recognition of coal and rock is an important prerequisite for safe and efficient coal mining.In this paper,a novel coal-rock recognition method is proposed based on fusing laser point cloud and images,named Multi-Modal Frustum PointNet(MMFP).Firstly,MobileNetV3 is used as the backbone network of Mask R-CNN to reduce the network parameters and compress the model volume.The dilated convolutional block attention mechanism(Dilated CBAM)and inception structure are combined with MobileNetV3 to further enhance the detection accuracy.Subsequently,the 2D target candidate box is calculated through the improved Mask R-CNN,and the frustum point cloud in the 2D target candidate box is extracted to reduce the calculation scale and spatial search range.Then,the self-attention PointNet is constructed to segment the fused point cloud within the frustum range,and the bounding box regression network is used to predict the bounding box parameters.Finally,an experimental platform of shearer coal wall cutting is established,and multiple comparative experiments are conducted.Experimental results indicate that the proposed coal-rock recognition method is superior to other advanced models.
基金This work was supported in part by the Gansu Provincial Science and Technology Major Special Innovation Consortium Project(No.21ZD3GA002).
文摘3D object detection is a critical technology in many applications,and among the various detection methods,pointcloud-based methods have been the most popular research topic in recent years.Since Graph Neural Network(GNN)is considered to be effective in dealing with pointclouds,in this work,we combined it with the attention mechanism and proposed a 3D object detection method named PointGAT.Our proposed PointGAT outperforms previous approaches on the KITTI test dataset.Experiments in real campus scenarios also demonstrate the potential of our method for further applications.
