3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,su...Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.展开更多
Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To addre...Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To address this,we present SCENET-3D,a transformer-drivenmultimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline.In the first stage,scene analysis,rich geometric and texture descriptors are extracted from RGB frames,including surface-normal histograms,angles between neighboring normals,Zernike moments,directional standard deviation,and Gabor-filter responses.In the second stage,scene-object analysis,non-human objects are segmented and represented using local feature descriptors and complementary surface-normal information.In the third stage,human-pose estimation,silhouettes are processed through an enhanced MoveNet to obtain 2D anatomical keypoints,which are fused with depth information and converted into RGB-based point clouds to construct pseudo-3D skeletons.Features from all three stages are fused and fed in a transformer encoder with multi-head attention to resolve visually similar activities.Experiments on UCLA(95.8%),ETRI-Activity3D(89.4%),andCAD-120(91.2%)demonstrate that combining pseudo-3D skeletonswith rich scene-object fusion significantly improves generalizable activity recognition,enabling safer elderly care,natural human–robot interaction,and robust context-aware robotic perception in real-world environments.展开更多
Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a nov...Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.展开更多
This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts som...This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.展开更多
It is very important to locate and track weather systems which cause severe calamity,such as severeconvective clouds (SCC),in nowcasting. In this paper the recognition and tracking of SCC is studied withGMS IR images ...It is very important to locate and track weather systems which cause severe calamity,such as severeconvective clouds (SCC),in nowcasting. In this paper the recognition and tracking of SCC is studied withGMS IR images using computer image techniques. As an IR image preprocessing, a SCC futerlng algorithm is put forward that combines a segment smoothing filtering and a removal procedure by thresholds. To the filtered SCCs the T algorithm and IP algorithm of contour coding method are applied to extract the contour line and its initial point. The description of SCCs includes four characteristic quanti-ties, i. e. center of gravity, cloud size, moment invariant M and R-shaped descriptor. Pattern recosnitionand pattern matching techniques are used to track the SCCs. Two procedures of rough and fine matchingare given. The former procedure include the setting of searching area and recognition of area and the latter is composed by the matching of shape descriptor R and moment invariant M and the analysis of correlative brightness temperature analysis.展开更多
Point cloud based place recognition plays an important role in mobile robotics. In this paper, we propose a weighted aggregation method from structure information adaptively for point cloud place recognition. Firstly,...Point cloud based place recognition plays an important role in mobile robotics. In this paper, we propose a weighted aggregation method from structure information adaptively for point cloud place recognition. Firstly, to preserve the prior distributions and local geometric structures, we fuse learned hidden features with handcrafted features in the beginning. Secondly, we further extract and aggregate adaptively weighted features concerning density and relative spatial information from these fused features, named Weighted Aggregation with Density Estimation (WADE) module. Then, we conduct the WADE block iteratively to group the latent manifold structures. Finally, comparison results on two public datasets Oxford Robotcar and KITTI show that the proposed approach exceeds the comparison approaches on recall rate averagely 7% - 8%.展开更多
The satellite laser ranging （SLR） data quality from the COMPASS was analyzed, and the difference between curve recognition in computer vision and pre-process of SLR data finally proposed a new algorithm for SLR was ...The satellite laser ranging （SLR） data quality from the COMPASS was analyzed, and the difference between curve recognition in computer vision and pre-process of SLR data finally proposed a new algorithm for SLR was discussed data based on curve recognition from points cloud is proposed. The results obtained by the new algorithm are 85 % （or even higher） consistent with that of the screen displaying method, furthermore, the new method can process SLR data automatically, which makes it possible to be used in the development of the COMPASS navigation system.展开更多
Understanding the conformational characteristics of polymers is key to elucidating their physical properties.Cyclic polymers,defined by their closed-loop structures,inherently differ from linear polymers possessing di...Understanding the conformational characteristics of polymers is key to elucidating their physical properties.Cyclic polymers,defined by their closed-loop structures,inherently differ from linear polymers possessing distinct chain ends.Despite these structural differences,both types of polymers exhibit locally random-walk-like conformations,making it challenging to detect subtle spatial variations using conventional methods.In this study,we address this challenge by integrating molecular dynamics simulations with point cloud neural networks to analyze the spatial conformations of cyclic and linear polymers.By utilizing the Dynamic Graph CNN(DGCNN)model,we classify polymer conformations based on the 3D coordinates of monomers,capturing local and global topological differences without considering chain connectivity sequentiality.Our findings reveal that the optimal local structural feature unit size scales linearly with molecular weight,aligning with theoretical predictions.Additionally,interpretability techniques such as Grad-CAM and SHAP identify significant conformational differences:cyclic polymers tend to form prolate ellipsoid shapes with pronounced elongation along the major axis,while linear polymers show elongated ends with more spherical centers.These findings reveal subtle yet critical differences in local conformations between cyclic and linear polymers that were previously difficult to discern,providing deeper insights into polymer structure-property relationships and offering guidance for future polymer science advancements.展开更多
Automatic identificationof discontinuities is a key focus in rock slope research.Conventional methods typically target small areas,which limits efficiencyand applicability for complex discontinuities in large-scale ro...Automatic identificationof discontinuities is a key focus in rock slope research.Conventional methods typically target small areas,which limits efficiencyand applicability for complex discontinuities in large-scale rock slopes.This study uses multi-angle unmanned aerial vehicle(UAV)nap-of-the-object photogrammetry to construct a high-definitionthree-dimensional(3D)point cloud model of the slope.The edge-firstconnection algorithm identifiesall edge points of discontinuities in the point cloud and completes recognition through simple connection analysis.This method avoids the complex calculations required for sequentially identifying discontinuity edges in conventional methods and achieves significantacceleration through algorithm optimization and parallel computation support.Based on this algorithm,the RockDiscontinuity Identification(RD ID)software is developed and applied to identify numerous highly disordered discontinuities on the Xulong slope in the Jinsha River suture zone.Processing tens of millions of point clouds within approximately 2 h demonstrates exceptional computational efficiency.The automatic algorithm accurately identifiesnearly 80%of planar discontinuities,with orientations and trace lengths closely matching manual results,highlighting its potential for large-scale rock outcrop applications.Comparisons with region growing algorithms further emphasize its effectiveness and accuracy.However,the algorithm struggles to identify linear discontinuities,which are a major source of error.Additionally,high roughness and smooth edges of discontinuities affect recognition accuracy,indicating areas for further improvement.展开更多
针对卫星拒止环境下无人车在未知区域自主定位难题,提出一种从航空图像到地面点云的跨模态地点识别方法,并设计相应的网络架构(Aerial-to-Ground Position Recognition Network, AG-PRNet)。该方法通过数据预处理将点云投影到鸟瞰视图(B...针对卫星拒止环境下无人车在未知区域自主定位难题,提出一种从航空图像到地面点云的跨模态地点识别方法,并设计相应的网络架构(Aerial-to-Ground Position Recognition Network, AG-PRNet)。该方法通过数据预处理将点云投影到鸟瞰视图(Bird's Eye View, BEV)空间,减小其与航空图像的模态差异;设计旋转平移不变特征编码模块(Rotation And Translation Invariant CNN,RATI-CNN),提取跨模态数据的旋转平移不变特征;利用交叉注意力模块融合学习跨模态数据的共享特征,提升特征匹配的鲁棒性。在自建跨网域地点识别(Cross-Domain Place Recognition, CDPR)数据集上的实验表明,所提方法的Top-1和Top-5召回率分别达60.08%和76%,显著优于基线方法,验证了其在跨模态地点识别中的有效性。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
基金the National Key Research and Development Program of China(Grant No.2023YFC3009400).
文摘Discontinuities in rock masses critically impact the stability and safety of underground engineering.Mainstream discontinuities identificationmethods,which rely on normal vector estimation and clustering algorithms,suffer from accuracy degradation,omission of critical discontinuities when orientation density is unevenly distributed,and need manual intervention.To overcome these limitations,this paper introduces a novel discontinuities identificationmethod based on geometric feature analysis of rock mass.By analyzing spatial distribution variability of point cloud and integrating an adaptive region growing algorithm,the method accurately detects independent discontinuities under complex geological conditions.Given that rock mass orientations typically follow a Fisher distribution,an adaptive hierarchical clustering algorithm based on statistical analysis is employed to automatically determine the optimal number of structural sets,eliminating the need for preset clusters or thresholds inherent in traditional methods.The proposed approach effectively handles diverse rock mass shapes and sizes,leveraging both local and global geometric features to minimize noise interference.Experimental validation on three real-world rock mass models,alongside comparisons with three conventional directional clustering algorithms,demonstrates superior accuracy and robustness in identifying optimal discontinuity sets.The proposed method offers a reliable and efficienttool for discontinuities detection and grouping in underground engineering,significantlyenhancing design and construction outcomes.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R410),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Human object detection and recognition is essential for elderly monitoring and assisted living however,models relying solely on pose or scene context often struggle in cluttered or visually ambiguous settings.To address this,we present SCENET-3D,a transformer-drivenmultimodal framework that unifies human-centric skeleton features with scene-object semantics for intelligent robotic vision through a three-stage pipeline.In the first stage,scene analysis,rich geometric and texture descriptors are extracted from RGB frames,including surface-normal histograms,angles between neighboring normals,Zernike moments,directional standard deviation,and Gabor-filter responses.In the second stage,scene-object analysis,non-human objects are segmented and represented using local feature descriptors and complementary surface-normal information.In the third stage,human-pose estimation,silhouettes are processed through an enhanced MoveNet to obtain 2D anatomical keypoints,which are fused with depth information and converted into RGB-based point clouds to construct pseudo-3D skeletons.Features from all three stages are fused and fed in a transformer encoder with multi-head attention to resolve visually similar activities.Experiments on UCLA(95.8%),ETRI-Activity3D(89.4%),andCAD-120(91.2%)demonstrate that combining pseudo-3D skeletonswith rich scene-object fusion significantly improves generalizable activity recognition,enabling safer elderly care,natural human–robot interaction,and robust context-aware robotic perception in real-world environments.
基金Project(LJRC013)supported by the University Innovation Team of Hebei Province Leading Talent Cultivation,China
文摘Flatness pattern recognition is the key of the flatness control. The accuracy of the present flatness pattern recognition is limited and the shape defects cannot be reflected intuitively. In order to improve it, a novel method via T-S cloud inference network optimized by genetic algorithm(GA) is proposed. T-S cloud inference network is constructed with T-S fuzzy neural network and the cloud model. So, the rapid of fuzzy logic and the uncertainty of cloud model for processing data are both taken into account. What's more, GA possesses good parallel design structure and global optimization characteristics. Compared with the simulation recognition results of traditional BP Algorithm, GA is more accurate and effective. Moreover, virtual reality technology is introduced into the field of shape control by Lab VIEW, MATLAB mixed programming. And virtual flatness pattern recognition interface is designed.Therefore, the data of engineering analysis and the actual model are combined with each other, and the shape defects could be seen more lively and intuitively.
文摘This paper presents a method for hand gesture recognition based on 3D point cloud. Digital image processing technology is used in this research. Based on the 3D point from depth camera, the system firstly extracts some raw data of the hand. After the data segmentation and preprocessing, three kinds of appearance features are extracted, including the number of stretched fingers, the angles between fingers and the gesture region’s area distribution feature. Based on these features, the system implements the identification of the gestures by using decision tree method. The results of experiment demonstrate that the proposed method is pretty efficient to recognize common gestures with a high accuracy.
文摘It is very important to locate and track weather systems which cause severe calamity,such as severeconvective clouds (SCC),in nowcasting. In this paper the recognition and tracking of SCC is studied withGMS IR images using computer image techniques. As an IR image preprocessing, a SCC futerlng algorithm is put forward that combines a segment smoothing filtering and a removal procedure by thresholds. To the filtered SCCs the T algorithm and IP algorithm of contour coding method are applied to extract the contour line and its initial point. The description of SCCs includes four characteristic quanti-ties, i. e. center of gravity, cloud size, moment invariant M and R-shaped descriptor. Pattern recosnitionand pattern matching techniques are used to track the SCCs. Two procedures of rough and fine matchingare given. The former procedure include the setting of searching area and recognition of area and the latter is composed by the matching of shape descriptor R and moment invariant M and the analysis of correlative brightness temperature analysis.
文摘Point cloud based place recognition plays an important role in mobile robotics. In this paper, we propose a weighted aggregation method from structure information adaptively for point cloud place recognition. Firstly, to preserve the prior distributions and local geometric structures, we fuse learned hidden features with handcrafted features in the beginning. Secondly, we further extract and aggregate adaptively weighted features concerning density and relative spatial information from these fused features, named Weighted Aggregation with Density Estimation (WADE) module. Then, we conduct the WADE block iteratively to group the latent manifold structures. Finally, comparison results on two public datasets Oxford Robotcar and KITTI show that the proposed approach exceeds the comparison approaches on recall rate averagely 7% - 8%.
文摘The satellite laser ranging （SLR） data quality from the COMPASS was analyzed, and the difference between curve recognition in computer vision and pre-process of SLR data finally proposed a new algorithm for SLR was discussed data based on curve recognition from points cloud is proposed. The results obtained by the new algorithm are 85 % （or even higher） consistent with that of the screen displaying method, furthermore, the new method can process SLR data automatically, which makes it possible to be used in the development of the COMPASS navigation system.
基金the National Key R&D Program of China(No.2022YFB3707303)National Natural Science Foundation of China(No.52293471)。
文摘Understanding the conformational characteristics of polymers is key to elucidating their physical properties.Cyclic polymers,defined by their closed-loop structures,inherently differ from linear polymers possessing distinct chain ends.Despite these structural differences,both types of polymers exhibit locally random-walk-like conformations,making it challenging to detect subtle spatial variations using conventional methods.In this study,we address this challenge by integrating molecular dynamics simulations with point cloud neural networks to analyze the spatial conformations of cyclic and linear polymers.By utilizing the Dynamic Graph CNN(DGCNN)model,we classify polymer conformations based on the 3D coordinates of monomers,capturing local and global topological differences without considering chain connectivity sequentiality.Our findings reveal that the optimal local structural feature unit size scales linearly with molecular weight,aligning with theoretical predictions.Additionally,interpretability techniques such as Grad-CAM and SHAP identify significant conformational differences:cyclic polymers tend to form prolate ellipsoid shapes with pronounced elongation along the major axis,while linear polymers show elongated ends with more spherical centers.These findings reveal subtle yet critical differences in local conformations between cyclic and linear polymers that were previously difficult to discern,providing deeper insights into polymer structure-property relationships and offering guidance for future polymer science advancements.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3080200)the China Postdoctoral Science Foundation(Grant No.2023M731264)the Science and Technology Development Plan Project of Jilin Province,China(Grant No.20250602007RC).
文摘Automatic identificationof discontinuities is a key focus in rock slope research.Conventional methods typically target small areas,which limits efficiencyand applicability for complex discontinuities in large-scale rock slopes.This study uses multi-angle unmanned aerial vehicle(UAV)nap-of-the-object photogrammetry to construct a high-definitionthree-dimensional(3D)point cloud model of the slope.The edge-firstconnection algorithm identifiesall edge points of discontinuities in the point cloud and completes recognition through simple connection analysis.This method avoids the complex calculations required for sequentially identifying discontinuity edges in conventional methods and achieves significantacceleration through algorithm optimization and parallel computation support.Based on this algorithm,the RockDiscontinuity Identification(RD ID)software is developed and applied to identify numerous highly disordered discontinuities on the Xulong slope in the Jinsha River suture zone.Processing tens of millions of point clouds within approximately 2 h demonstrates exceptional computational efficiency.The automatic algorithm accurately identifiesnearly 80%of planar discontinuities,with orientations and trace lengths closely matching manual results,highlighting its potential for large-scale rock outcrop applications.Comparisons with region growing algorithms further emphasize its effectiveness and accuracy.However,the algorithm struggles to identify linear discontinuities,which are a major source of error.Additionally,high roughness and smooth edges of discontinuities affect recognition accuracy,indicating areas for further improvement.
文摘针对卫星拒止环境下无人车在未知区域自主定位难题,提出一种从航空图像到地面点云的跨模态地点识别方法,并设计相应的网络架构(Aerial-to-Ground Position Recognition Network, AG-PRNet)。该方法通过数据预处理将点云投影到鸟瞰视图(Bird's Eye View, BEV)空间,减小其与航空图像的模态差异;设计旋转平移不变特征编码模块(Rotation And Translation Invariant CNN,RATI-CNN),提取跨模态数据的旋转平移不变特征;利用交叉注意力模块融合学习跨模态数据的共享特征,提升特征匹配的鲁棒性。在自建跨网域地点识别(Cross-Domain Place Recognition, CDPR)数据集上的实验表明,所提方法的Top-1和Top-5召回率分别达60.08%和76%,显著优于基线方法,验证了其在跨模态地点识别中的有效性。
