This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network models.These point clouds,which represent spatial information throu...This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network models.These point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging applications.Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities.Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds.However,there has been a lack of focus on making the most of the numerous existing augmentation techniques.Addressing this deficiency,this research investigates the possibility of combining two fundamental data augmentation strategies.The paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named RandomFusion.Instead of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or sample.This innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or Mix3D.The crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data set.The results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation tasks.This is achieved without compromising computational efficiency.By examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point clouds.RandomFusion data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the robustness of models.The insights gained from this research can pave the way for future work aimed at developing more advanced and efficient data augmentation strategies for 3D lidar point cloud analysis.展开更多
Ventilation system analysis for underground mines has remained mostly unchanged since the Atkinson method was made popular by Mc Elroy in 1935. Data available to ventilation technicians and engineers is typically limi...Ventilation system analysis for underground mines has remained mostly unchanged since the Atkinson method was made popular by Mc Elroy in 1935. Data available to ventilation technicians and engineers is typically limited to the quantity of air moving through any given heading. Because computer-aided modelling, simulation, and ventilation system design tools have improved, it is now important to ensure that developed models have the most accurate information possible. This paper presents a new technique for estimating underground drift friction factors that works by processing 3 D point cloud data obtained by using a mobile Li DAR. Presented are field results that compare the proposed approach with previously published algorithms, as well as with manually acquired measurements.展开更多
Nowadays,the deep learning for object detection has become more popular and is widely adopted in many fields.This paper focuses on the research of LiDAR and camera sensor fusion technology for vehicle detection to ens...Nowadays,the deep learning for object detection has become more popular and is widely adopted in many fields.This paper focuses on the research of LiDAR and camera sensor fusion technology for vehicle detection to ensure extremely high detection accuracy.The proposed network architecture takes full advantage of the deep information of both the LiDAR point cloud and RGB image in object detection.First,the LiDAR point cloud and RGB image are fed into the system.Then a high-resolution feature map is used to generate a reliable 3D object proposal for both the LiDAR point cloud and RGB image.Finally,3D box regression is performed to predict the extent and orientation of vehicles in 3D space.Experiments on the challenging KITTI benchmark show that the proposed approach obtains ideal detection results and the detection time of each frame is about 0.12 s.This approach could establish a basis for further research in autonomous vehicles.展开更多
Main cable line shape measurement and parameter identification are a critical task in the construction monitoring and service maintenance of suspension bridges.3D LiDAR scanning can simultaneously obtain the coordinat...Main cable line shape measurement and parameter identification are a critical task in the construction monitoring and service maintenance of suspension bridges.3D LiDAR scanning can simultaneously obtain the coordinates of multiple points on the target,offering high accuracy and efficiency.As a result,it is expected to be used in applications requiring rapid,large-scale measurements,such as main cable line shape measurement for suspension bridges.However,due to the large span and tall main towers of suspension bridges,the LiDAR field of view often encounters obstructions,making it difficult to obtain high-quality point clouds for the entire bridge.The collected point clouds are typically unevenly distributed and of poor quality.Therefore,LiDAR is used to monitor the local cable line shape.This paper proposes an innovative non-uniform sampling method that adjusts the sampling density based on the main cable’s rate of change.Additionally,the Random Sample Consensus(RANSAC)algorithm,the ordinary least squares,and center-of-mass calibration are applied to identify and optimize the geometric parameters of the cross-section point cloud of the main cable.Given the strong design prior information available during suspension bridge construction,Bayesian theory is applied to predict and adjust the global line shape of the main cable.The study shows that using LiDAR for cable point cloud measurement enables rapid acquisition of high-precision point cloud data,significantly enhancing data collection efficiency.The method proposed in this paper offers advantages such as highly automated,low risk,low cost,and sustainability,making it suitable for green monitoring throughout the entire main cable construction process.展开更多
基金funded in part by the Key Project of Nature Science Research for Universities of Anhui Province of China(No.2022AH051720)in part by the Science and Technology Development Fund,Macao SAR(Grant Nos.0093/2022/A2,0076/2022/A2 and 0008/2022/AGJ)in part by the China University Industry-University-Research Collaborative Innovation Fund(No.2021FNA04017).
文摘This paper focuses on the effective utilization of data augmentation techniques for 3Dlidar point clouds to enhance the performance of neural network models.These point clouds,which represent spatial information through a collection of 3D coordinates,have found wide-ranging applications.Data augmentation has emerged as a potent solution to the challenges posed by limited labeled data and the need to enhance model generalization capabilities.Much of the existing research is devoted to crafting novel data augmentation methods specifically for 3D lidar point clouds.However,there has been a lack of focus on making the most of the numerous existing augmentation techniques.Addressing this deficiency,this research investigates the possibility of combining two fundamental data augmentation strategies.The paper introduces PolarMix andMix3D,two commonly employed augmentation techniques,and presents a new approach,named RandomFusion.Instead of using a fixed or predetermined combination of augmentation methods,RandomFusion randomly chooses one method from a pool of options for each instance or sample.This innovative data augmentation technique randomly augments each point in the point cloud with either PolarMix or Mix3D.The crux of this strategy is the random choice between PolarMix and Mix3Dfor the augmentation of each point within the point cloud data set.The results of the experiments conducted validate the efficacy of the RandomFusion strategy in enhancing the performance of neural network models for 3D lidar point cloud semantic segmentation tasks.This is achieved without compromising computational efficiency.By examining the potential of merging different augmentation techniques,the research contributes significantly to a more comprehensive understanding of how to utilize existing augmentation methods for 3D lidar point clouds.RandomFusion data augmentation technique offers a simple yet effective method to leverage the diversity of augmentation techniques and boost the robustness of models.The insights gained from this research can pave the way for future work aimed at developing more advanced and efficient data augmentation strategies for 3D lidar point cloud analysis.
基金supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) under grant CRDPJ 44580412Barrick Gold Corporation and Peck Tech Consulting Ltd
文摘Ventilation system analysis for underground mines has remained mostly unchanged since the Atkinson method was made popular by Mc Elroy in 1935. Data available to ventilation technicians and engineers is typically limited to the quantity of air moving through any given heading. Because computer-aided modelling, simulation, and ventilation system design tools have improved, it is now important to ensure that developed models have the most accurate information possible. This paper presents a new technique for estimating underground drift friction factors that works by processing 3 D point cloud data obtained by using a mobile Li DAR. Presented are field results that compare the proposed approach with previously published algorithms, as well as with manually acquired measurements.
基金This work was supported by the National Key Research and Development Program of China(2017YFB0102603,2018YFB0105003)the National Natural Science Foundation of China(51875255,61601203,61773184,U1564201,U1664258,U1764257,U1762264)+3 种基金the Natural Science Foundation of Jiangsu Province(BK20180100)the Six Talent Peaks Project of Jiangsu Province(2018-TD-GDZB-022)the Key Project for the Development of Strategic Emerging Industries of Jiangsu Province(2016-1094)the Key Research and Development Program of Zhenjiang City(GY2017006).
文摘Nowadays,the deep learning for object detection has become more popular and is widely adopted in many fields.This paper focuses on the research of LiDAR and camera sensor fusion technology for vehicle detection to ensure extremely high detection accuracy.The proposed network architecture takes full advantage of the deep information of both the LiDAR point cloud and RGB image in object detection.First,the LiDAR point cloud and RGB image are fed into the system.Then a high-resolution feature map is used to generate a reliable 3D object proposal for both the LiDAR point cloud and RGB image.Finally,3D box regression is performed to predict the extent and orientation of vehicles in 3D space.Experiments on the challenging KITTI benchmark show that the proposed approach obtains ideal detection results and the detection time of each frame is about 0.12 s.This approach could establish a basis for further research in autonomous vehicles.
基金funded by the 2024 STCSM Shanghai Natural Science Grants General Project"Online Intelligent Perception and Warning of Large span Structural Vortex Vibration Based on Structural Health Monitoring"and the Science and Technology Project"Research on Intelligent Monitoring System Scheme for Large span Bridges in Mountainous Areas"of PowerChina Road Bridge Group Co.,Ltd.
文摘Main cable line shape measurement and parameter identification are a critical task in the construction monitoring and service maintenance of suspension bridges.3D LiDAR scanning can simultaneously obtain the coordinates of multiple points on the target,offering high accuracy and efficiency.As a result,it is expected to be used in applications requiring rapid,large-scale measurements,such as main cable line shape measurement for suspension bridges.However,due to the large span and tall main towers of suspension bridges,the LiDAR field of view often encounters obstructions,making it difficult to obtain high-quality point clouds for the entire bridge.The collected point clouds are typically unevenly distributed and of poor quality.Therefore,LiDAR is used to monitor the local cable line shape.This paper proposes an innovative non-uniform sampling method that adjusts the sampling density based on the main cable’s rate of change.Additionally,the Random Sample Consensus(RANSAC)algorithm,the ordinary least squares,and center-of-mass calibration are applied to identify and optimize the geometric parameters of the cross-section point cloud of the main cable.Given the strong design prior information available during suspension bridge construction,Bayesian theory is applied to predict and adjust the global line shape of the main cable.The study shows that using LiDAR for cable point cloud measurement enables rapid acquisition of high-precision point cloud data,significantly enhancing data collection efficiency.The method proposed in this paper offers advantages such as highly automated,low risk,low cost,and sustainability,making it suitable for green monitoring throughout the entire main cable construction process.
