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
Sorghum canopy architecture in field trials is determined by various phenotypic traits,such plant and panicle count,leaf density and angle and panicle morphology,and canopy height.These traits together affect light ca...Sorghum canopy architecture in field trials is determined by various phenotypic traits,such plant and panicle count,leaf density and angle and panicle morphology,and canopy height.These traits together affect light capture and biomass production as well as conversion of photosynthates to grain yield.Panicle morphology ex-hibits considerable variation as influenced by genetics,environmental conditions and management practices.This study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology.First,we developed a scalable,low-altitude Unmanned Aerial Vehicle(UAV)-based protocol that leverages videos for efficient data acquisition,combined with Neural Radiance Fields(NeRF)s to generate high-quality 3D point cloud reconstructions of sorghum canopies.Next,a 3D model was built to simulate 3D sorghum canopies to create annotated datasets for training deep learning-based semantic segmentation and panicle detection algorithms.Finally,we propose SegVoteNet,a novel multi-task deep learning model that integrates VoteNet and PointNet++within a shared backbone architecture.Designed for semantic segmentation and 3D detection on pure point cloud data,SegVoteNet incorporates a voting and sampling module that leverages segmentation results to optimize object proposal generation.SegVoteNet is robust,achieving 0.986 Mean Average Precision(mAP)@0.5 Inter-section Over Union(IOU)on synthetic datasets,and 0.850 mAP@0.5 IOU on real point cloud datasets for sorghum panicle detection,without fine-tuning.This set of pipelines provides a robust scalable method for phenotyping sorghum panicles in field trials in breeding and commercial applications.Further work is developing a capability to estimate grain number per panicle,which would provide breeders with additional phenotypes to select.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 project was funded in part by the Grains Research and Development Corporation(GRDC)of Australia UOQ2003-011RTX‘Innovations in plant testing in Australia’
文摘Sorghum canopy architecture in field trials is determined by various phenotypic traits,such plant and panicle count,leaf density and angle and panicle morphology,and canopy height.These traits together affect light capture and biomass production as well as conversion of photosynthates to grain yield.Panicle morphology ex-hibits considerable variation as influenced by genetics,environmental conditions and management practices.This study presents a framework for the 3D reconstruction of sorghum canopies and phenotyping panicle morphology.First,we developed a scalable,low-altitude Unmanned Aerial Vehicle(UAV)-based protocol that leverages videos for efficient data acquisition,combined with Neural Radiance Fields(NeRF)s to generate high-quality 3D point cloud reconstructions of sorghum canopies.Next,a 3D model was built to simulate 3D sorghum canopies to create annotated datasets for training deep learning-based semantic segmentation and panicle detection algorithms.Finally,we propose SegVoteNet,a novel multi-task deep learning model that integrates VoteNet and PointNet++within a shared backbone architecture.Designed for semantic segmentation and 3D detection on pure point cloud data,SegVoteNet incorporates a voting and sampling module that leverages segmentation results to optimize object proposal generation.SegVoteNet is robust,achieving 0.986 Mean Average Precision(mAP)@0.5 Inter-section Over Union(IOU)on synthetic datasets,and 0.850 mAP@0.5 IOU on real point cloud datasets for sorghum panicle detection,without fine-tuning.This set of pipelines provides a robust scalable method for phenotyping sorghum panicles in field trials in breeding and commercial applications.Further work is developing a capability to estimate grain number per panicle,which would provide breeders with additional phenotypes to select.
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.
