Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have pr...Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have predominantly focused on landslides that occur on land.To this end,we aim to investigate ashore and underwater landslide data synchronously.This study proposes an optimized mosaicking method for ashore and underwater landslide data.This method fuses an airborne laser point cloud with multi-beam depth sounder images.Owing to their relatively high efficiency and large coverage area,airborne laser measurement systems are suitable for emergency investigations of landslides.Based on the airborne laser point cloud,the traversal of the point with the lowest elevation value in the point set can be used to perform rapid extraction of the crude channel boundaries.Further meticulous extraction of the channel boundaries is then implemented using the probability mean value optimization method.In addition,synthesis of the integrated ashore and underwater landslide data angle is realized using the spatial guide line between the channel boundaries and the underwater multibeam sonar images.A landslide located on the right bank of the middle reaches of the Yalong River is selected as a case study to demonstrate that the proposed method has higher precision thantraditional methods.The experimental results show that the mosaicking method in this study can meet the basic needs of landslide modeling and provide a basis for qualitative and quantitative analysis and stability prediction of landslides.展开更多
In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strat...In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.展开更多
Pedestrian detection is a critical problem in the field of computer vision. Although most existing algorithms are able to detect pedestrians well in controlled environ- ments, it is often difficult to achieve accurate...Pedestrian detection is a critical problem in the field of computer vision. Although most existing algorithms are able to detect pedestrians well in controlled environ- ments, it is often difficult to achieve accurate pedestrian de- tection from video sequences alone, especially in pedestrian- intensive scenes wherein pedestrians may cause mutual oc- clusion and thus incomplete detection. To surmount these dif- ficulties, this paper presents pedestrian detection algorithm based on video sequences and laser point cloud. First, laser point cloud is interpreted and classified to separate pedes- trian data and vehicle data. Then a fusion of video image data and laser point cloud data is achieved by calibration. The re- gion of interest after fusion is determined using feature in- formation contained in video image and three-dimensional information of laser point cloud to remove false detection of pedestrian and thus to achieve pedestrian detection in inten- sive scenes. Experimental verification and analysis in video sequences demonstrate that fusion of two data improves the performance of pedestrian detection and has better detection results.展开更多
For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by th...For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.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
To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-sca...To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.展开更多
三维激光扫描技术作为一种先进的测量手段应用前景十分广阔,但是,在应用其扫描所得的点云数据进行内处理上又遇到了许多技术性的问题。CAD系统处理大量点云数据过程中存在局限性,一旦用CAD系统处理点云时,CAD程序就会出现错误操作提示,...三维激光扫描技术作为一种先进的测量手段应用前景十分广阔,但是,在应用其扫描所得的点云数据进行内处理上又遇到了许多技术性的问题。CAD系统处理大量点云数据过程中存在局限性,一旦用CAD系统处理点云时,CAD程序就会出现错误操作提示,甚至完全停止进程。CloudWorx克服了这些局限性,它避免了直接输入数据,而是利用Cyclone技术作为MicroStation环境下有效管理和解决点云的工具,使MicroStation内点云操作与CAD程序执行再无冲突。本文主要介绍了并且还介绍了CloudWorx模块的功能,并举例就点云数据在CloudWorx for MicroStation软件环境下的处理工作进行了详细的介绍。展开更多
The appearance of 3D laser scanning technology is one of the most important technology revolutions in surveying and mapping field. It can be widely used in many interrelated fields, such as engineering constructions a...The appearance of 3D laser scanning technology is one of the most important technology revolutions in surveying and mapping field. It can be widely used in many interrelated fields, such as engineering constructions and 3D measurements, owing to its prominent characteristics of the high efficiency and high precision. At present its application is still in the initial state, and it is quite rarely used in China, especially in geotechnical engineering and geological engineering fields. Starting with a general introduction of 3D laser scanning technology, this article studies how to apply the technology to high rock slope investigations. By way of a case study, principles and methods of quick slope documentation and occurrence measurement of discontinuities are discussed and analyzed. Analysis results show that the application of 3D laser scanning technology to geotechnical and geological engineering has a great prospect and value.展开更多
Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between le...Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between leaves and nonphotosynthetic structures to retrieve the leaf normal has been challenging. We used here an approach to accurately retrieve the leaf normals of camphorwood（Cinnamomum camphora） using TLS point cloud data.First, nonphotosynthetic structures were filtered by using the curvature threshold of each point. Then, the point cloud data were segmented by a voxel method and clustered by a Gaussian mixture model in each voxel. Finally, the normal vector of each cluster was computed by principal component analysis to obtain the leaf normal distribution. We collected leaf inclination angles and estimated the distribution, which we compared with the retrieved leaf normal distribution. The correlation coefficient between measurements and obtained results was 0.96, indicating a good coincidence.展开更多
基金supported in part by the National Key R&D Program of China(Grant no.2016YFC0401908)。
文摘Landslides are one of the most disastrous geological hazards in southwestern China.Once a landslide becomes unstable,it threatens the lives and safety of local residents.However,empirical studies on landslides have predominantly focused on landslides that occur on land.To this end,we aim to investigate ashore and underwater landslide data synchronously.This study proposes an optimized mosaicking method for ashore and underwater landslide data.This method fuses an airborne laser point cloud with multi-beam depth sounder images.Owing to their relatively high efficiency and large coverage area,airborne laser measurement systems are suitable for emergency investigations of landslides.Based on the airborne laser point cloud,the traversal of the point with the lowest elevation value in the point set can be used to perform rapid extraction of the crude channel boundaries.Further meticulous extraction of the channel boundaries is then implemented using the probability mean value optimization method.In addition,synthesis of the integrated ashore and underwater landslide data angle is realized using the spatial guide line between the channel boundaries and the underwater multibeam sonar images.A landslide located on the right bank of the middle reaches of the Yalong River is selected as a case study to demonstrate that the proposed method has higher precision thantraditional methods.The experimental results show that the mosaicking method in this study can meet the basic needs of landslide modeling and provide a basis for qualitative and quantitative analysis and stability prediction of landslides.
基金founded by National Key R&D Program of China (No.2021YFB2601200)National Natural Science Foundation of China (No.42171416)Teacher Support Program for Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture (No.JDJQ20200307).
文摘In light of the limited efficacy of conventional methods for identifying pavement cracks and the absence of comprehensive depth and location data in two-dimensional photographs,this study presents an intelligent strategy for extracting road cracks.This methodology involves the integration of laser point cloud data obtained from a vehicle-mounted system and a panoramic sequence of images.The study employs a vehicle-mounted LiDAR measurement system to acquire laser point cloud and panoramic sequence image data simultaneously.A convolutional neural network is utilized to extract cracks from the panoramic sequence image.The extracted sequence image is then aligned with the laser point cloud,enabling the assignment of RGB information to the vehicle-mounted three dimensional(3D)point cloud and location information to the two dimensional(2D)panoramic image.Additionally,a threshold value is set based on the crack elevation change to extract the aligned roadway point cloud.The three-dimensional data pertaining to the cracks can be acquired.The experimental findings demonstrate that the use of convolutional neural networks has yielded noteworthy outcomes in the extraction of road cracks.The utilization of point cloud and image alignment techniques enables the extraction of precise location data pertaining to road cracks.This approach exhibits superior accuracy when compared to conventional methods.Moreover,it facilitates rapid and accurate identification and localization of road cracks,thereby playing a crucial role in ensuring road maintenance and traffic safety.Consequently,this technique finds extensive application in the domains of intelligent transportation and urbanization development.The technology exhibits significant promise for use in the domains of intelligent transportation and city development.
文摘Pedestrian detection is a critical problem in the field of computer vision. Although most existing algorithms are able to detect pedestrians well in controlled environ- ments, it is often difficult to achieve accurate pedestrian de- tection from video sequences alone, especially in pedestrian- intensive scenes wherein pedestrians may cause mutual oc- clusion and thus incomplete detection. To surmount these dif- ficulties, this paper presents pedestrian detection algorithm based on video sequences and laser point cloud. First, laser point cloud is interpreted and classified to separate pedes- trian data and vehicle data. Then a fusion of video image data and laser point cloud data is achieved by calibration. The re- gion of interest after fusion is determined using feature in- formation contained in video image and three-dimensional information of laser point cloud to remove false detection of pedestrian and thus to achieve pedestrian detection in inten- sive scenes. Experimental verification and analysis in video sequences demonstrate that fusion of two data improves the performance of pedestrian detection and has better detection results.
文摘For a vision measurement system consisted of laser-CCD scanning sensors, an algorithm is proposed to extract and recognize the target object contour. Firstly, the two-dimensional（2D） point cloud that is output by the integrated laser sensor is transformed into a binary image. Secondly, the potential target object contours are segmented and extracted based on the connected domain labeling and adaptive corner detection. Then, the target object contour is recognized by improved Hu invariant moments and BP neural network classifier. Finally, we extract the point data of the target object contour through the reverse transformation from a binary image to a 2D point cloud. The experimental results show that the average recognition rate is 98.5% and the average recognition time is 0.18 s per frame. This algorithm realizes the real-time tracking of the target object in the complex background and the condition of multi-moving objects.
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.
文摘To address the current issues of inaccurate segmentation and the limited applicability of segmentation methods for building facades in point clouds, we propose a facade segmentation algorithm based on optimal dual-scale feature descriptors. First, we select the optimal dual-scale descriptors from a range of feature descriptors. Next, we segment the facade according to the threshold value of the chosen optimal dual-scale descriptors. Finally, we use RANSAC (Random Sample Consensus) to fit the segmented surface and optimize the fitting result. Experimental results show that, compared to commonly used facade segmentation algorithms, the proposed method yields more accurate segmentation results, providing a robust data foundation for subsequent 3D model reconstruction of buildings.
文摘三维激光扫描技术作为一种先进的测量手段应用前景十分广阔,但是,在应用其扫描所得的点云数据进行内处理上又遇到了许多技术性的问题。CAD系统处理大量点云数据过程中存在局限性,一旦用CAD系统处理点云时,CAD程序就会出现错误操作提示,甚至完全停止进程。CloudWorx克服了这些局限性,它避免了直接输入数据,而是利用Cyclone技术作为MicroStation环境下有效管理和解决点云的工具,使MicroStation内点云操作与CAD程序执行再无冲突。本文主要介绍了并且还介绍了CloudWorx模块的功能,并举例就点云数据在CloudWorx for MicroStation软件环境下的处理工作进行了详细的介绍。
基金the Key Project of Joint Funds of Yalongjiang River Development of the National Natural Science Foundation of China (No. 50539050)
文摘The appearance of 3D laser scanning technology is one of the most important technology revolutions in surveying and mapping field. It can be widely used in many interrelated fields, such as engineering constructions and 3D measurements, owing to its prominent characteristics of the high efficiency and high precision. At present its application is still in the initial state, and it is quite rarely used in China, especially in geotechnical engineering and geological engineering fields. Starting with a general introduction of 3D laser scanning technology, this article studies how to apply the technology to high rock slope investigations. By way of a case study, principles and methods of quick slope documentation and occurrence measurement of discontinuities are discussed and analyzed. Analysis results show that the application of 3D laser scanning technology to geotechnical and geological engineering has a great prospect and value.
文摘Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between leaves and nonphotosynthetic structures to retrieve the leaf normal has been challenging. We used here an approach to accurately retrieve the leaf normals of camphorwood（Cinnamomum camphora） using TLS point cloud data.First, nonphotosynthetic structures were filtered by using the curvature threshold of each point. Then, the point cloud data were segmented by a voxel method and clustered by a Gaussian mixture model in each voxel. Finally, the normal vector of each cluster was computed by principal component analysis to obtain the leaf normal distribution. We collected leaf inclination angles and estimated the distribution, which we compared with the retrieved leaf normal distribution. The correlation coefficient between measurements and obtained results was 0.96, indicating a good coincidence.
