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
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
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
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
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
在露天煤矿开采中,开采环境存在复杂多变等特点,矿山整体结构的改变导致的矿山边坡形变给安全生产带来重大隐患,也会给生态环境带来一定的破坏。当前对于矿山的变形监测虽逐渐趋向于自动化,但整个过程仍需依赖于人工或各种监测设备,且...在露天煤矿开采中,开采环境存在复杂多变等特点,矿山整体结构的改变导致的矿山边坡形变给安全生产带来重大隐患,也会给生态环境带来一定的破坏。当前对于矿山的变形监测虽逐渐趋向于自动化,但整个过程仍需依赖于人工或各种监测设备,且设备的维护比较困难,有的设备操作较为复杂、成本较高。为了更为简便直观地监测矿井边坡的形变信息,结合基于学习的点云配准方法实现了一种基于激光点云配准的矿井边坡变形监测方法。该方法首先提出了一种深度学习模型SA-RPE(Self-Attention with Relative Position Encoding)(相对位置编码的自注意力模型)在矿山数据集上实现了点云配准,并通过实验数据进行了验证;然后,根据配准的结果对矿井边坡进行了形变分析,并通过截取矿井边坡点云不同方向的断面进一步分析了各个断面的形变程度,结合二维断面图与三维点云渲染图的实验结果,表明深度学习模型SA-RPE能够比较准确地实现矿山激光点云的配准任务。通过分析深度学习模型预测的配准结果中旋转矩阵与平移向量的误差能够很好地掌握矿山的整体形变信息,而矿井边坡点云在不同方向上的断面图直观地展现了每一处形变的程度,计算不同时期断面点云对应点之间的平均距离能定量地描述各个断面的形变程度,通过阈值检测出来的异常值反映了断面上发生了较大形变的区域。实验结果体现了所提方法能够在满足实时性的基础上准确直观地表现出矿山边坡变形的信息。展开更多
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.
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.
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.
文摘在露天煤矿开采中,开采环境存在复杂多变等特点,矿山整体结构的改变导致的矿山边坡形变给安全生产带来重大隐患,也会给生态环境带来一定的破坏。当前对于矿山的变形监测虽逐渐趋向于自动化,但整个过程仍需依赖于人工或各种监测设备,且设备的维护比较困难,有的设备操作较为复杂、成本较高。为了更为简便直观地监测矿井边坡的形变信息,结合基于学习的点云配准方法实现了一种基于激光点云配准的矿井边坡变形监测方法。该方法首先提出了一种深度学习模型SA-RPE(Self-Attention with Relative Position Encoding)(相对位置编码的自注意力模型)在矿山数据集上实现了点云配准,并通过实验数据进行了验证;然后,根据配准的结果对矿井边坡进行了形变分析,并通过截取矿井边坡点云不同方向的断面进一步分析了各个断面的形变程度,结合二维断面图与三维点云渲染图的实验结果,表明深度学习模型SA-RPE能够比较准确地实现矿山激光点云的配准任务。通过分析深度学习模型预测的配准结果中旋转矩阵与平移向量的误差能够很好地掌握矿山的整体形变信息,而矿井边坡点云在不同方向上的断面图直观地展现了每一处形变的程度,计算不同时期断面点云对应点之间的平均距离能定量地描述各个断面的形变程度,通过阈值检测出来的异常值反映了断面上发生了较大形变的区域。实验结果体现了所提方法能够在满足实时性的基础上准确直观地表现出矿山边坡变形的信息。
