Target Detection on Water Surfaces Using Fusion of Camera and LiDAR Based Information

To address the challenges of missed detections in water surface target detection using solely visual algorithms in unmanned surface vehicle(USV)perception,this paper proposes a method based on the fusion of visual and LiDAR point-cloud projection for water surface target detection.Firstly,the visual recognition component employs an improved YOLOv7 algorithmbased on a self-built dataset for the detection of water surface targets.This algorithm modifies the original YOLOv7 architecture to a Slim-Neck structure,addressing the problemof excessive redundant information during feature extraction in the original YOLOv7 network model.Simultaneously,this modification simplifies the computational burden of the detector,reduces inference time,and maintains accuracy.Secondly,to tackle the issue of sample imbalance in the self-built dataset,slide loss function is introduced.Finally,this paper replaces the original Complete Intersection over Union(CIoU)loss function with the Minimum Point Distance Intersection over Union(MPDIoU)loss function in the YOLOv7 algorithm,which accelerates model learning and enhances robustness.To mitigate the problem of missed recognitions caused by complex water surface conditions in purely visual algorithms,this paper further adopts the fusion of LiDAR and camera data,projecting the threedimensional point-cloud data from LiDAR onto a two-dimensional pixel plane.This significantly reduces the rate of missed detections for water surface targets.

Point-cloud segmentation of individual trees in complex natural forest scenes based on a trunk-growth method

Forest resource management and ecological assessment have been recently supported by emerging technologies.Terrestrial laser scanning(TLS)is one that can be quickly and accurately used to obtain three-dimensional forest information,and create good representations of forest vertical structure.TLS data can be exploited for highly significant tasks,particularly the segmentation and information extraction for individual trees.However,the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness,and hence do not lead to satisfactory results for natural forests in complex environments.In this paper,we propose a trunk-growth(TG)method for single-tree point-cloud segmentation,and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan,China.First,the point normal vector and its Z-axis component are used as trunk-growth constraints.Then,the points surrounding the trunk are searched to account for regrowth.Finally,the nearest distributed branch and leaf points are used to complete the individual tree segmentation.The results show that the TG method can effectively segment individual trees with an average F-score of 0.96.The proposed method applies to many types of trees with various growth shapes,and can effectively identify shrubs and herbs in complex scenes of natural forests.The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.

GENERATE ROUGH TOOL-PATHS FROM UNORGANIZED POINT-CLOUD DIRECTLY

An approach is presented to generate rough interference-free tool-paths directly from massive unorganized data in rough machining that is performed by machining volumes of material in a slice-by-slice manner. Unorganized point-cloud is firstly converted to cross-section data. Then a robust data-structure named tool-path net is constructed to save tool-path data. Optimal algorithms for partitioning sub-cut-areas and computing interference-free cutter-locations are put forward. Finally the tool-paths are linked in a zigzag milling mode, which can be transformed into a traveling sales man problem. The experiment indicates optimal tool paths can be acquired, and high computation efficiency can be obtained and interference can be avoided successfully.

基于共线原理的全景影像真彩色点云生成算法

常规激光点云数据通过将激光点云与彩色影像进行融合,将影像所富含的色彩信息赋给相应的激光点云、可形成一种"纹理丰富、颜色直观、位置准确"的真彩色点云成果,不同类型相机拍摄得到的影像对激光点云赋彩色的原理和过程均不相同。文中基于移动测量系统及360°全景影像的测绘高新技术,研究全景相机拍摄的影像与激光点云的匹配、融合,提出一种基于共线原理的由全景影像给激光点云赋彩色的算法。算法充分利用全景影像360°可视以及激光点云位置精确的特点,经实验验证算法推导正确,生成的真彩色点云色彩纹理细致,空间位置准确。

手持式激光扫描点云与其内置影像的融合方法试验研究

针对手持式激光扫描仪ZEB-HORIZON采集的真彩色激光点云纹理效果不佳的问题,本文对该扫描点云与其内置影像进行融合,使生成的真彩色激光点云不仅具有空间信息和强度信息,还具有光谱信息。首先将该仪器内置相机同步采集到的影像通过SfM技术生成密集影像点云,然后采用激光点云与影像点云3D-3D的配准方法对两类点云进行匹配,再基于K-D树搜索进行纹理映射,最终生成真彩色的激光点云。试验结果表明,相比于在该仪器配套软件GeoSLAM Hub中自动生成的真彩色点云,本文方法生成的点云的纹理更清晰、视觉效果更好。

Digital Jacquard Fabric Design in Colorful Mode

Digital image design is one of advanced technique in textile design. The investigation into digital Jacquard textile design in the colorful mode is one form of research in digital Jacquard fabric design, which aimed at expanding past and present jacquard design and production methods towards innovative ends. In this paper, the design principles and design methods for unconventional digital Jacquard fabric design in colorful mode have been analyzed based on the new technologies and computer applied color theory. The results of this study will enhance further research in the area of digital textile.

Development of a Percentile Based Three-dimensional Model of the Buttocks in Computer System

There are diverse products related to human buttocks, which need to be designed, manufactured and evaluated with 3D buttock model. The 3D buttock model used in present research field is just simple approximate model similar to human buttocks. The 3D buttock percentile model is highly desired in the ergonomics design and evaluation for these products. So far, there is no research on the percentile sizing system of human 3D buttock model. So the purpose of this paper is to develop a new method for building three-dimensional buttock percentile model in computer system. After scanning the 3D shape of buttocks, the cloud data of 3D points is imported into the reverse engineering software（Geomagic） for the reconstructing of the buttock surface model. Five characteristic dimensions of the buttock are measured through mark-points after models being imported into engineering software CATIA. A series of space points are obtained by the intersecting of the cutting slices and 3D buttock surface model, and then are ordered based on the sequence number of the horizontal and vertical slices. The 1st, 5th, 50 th, 95 th, 99 th percentile values of the five dimensions and the spatial coordinate values of the space points are obtained, and used to reconstruct percentile buttock models. This research proposes a establishing method of percentile sizing system of buttock 3D model based on the percentile values of the ischial tuberosities diameter, the distances from margin to ischial tuberosity and the space coordinates value of coordinate points, for establishing the Nth percentile 3D buttock model and every special buttock types model. The proposed method also serves as a useful guidance for the other 3D percentile models establishment for other part in human body with characteristic points.

Panicle-3D: A low-cost 3D-modeling method for rice panicles based on deep learning, shape from silhouette, and supervoxel clustering

Self-occlusions are common in rice canopy images and strongly influence the calculation accuracies of panicle traits. Such interference can be largely eliminated if panicles are phenotyped at the 3 D level.Research on 3 D panicle phenotyping has been limited. Given that existing 3 D modeling techniques do not focus on specified parts of a target object, an efficient method for panicle modeling of large numbers of rice plants is lacking. This paper presents an automatic and nondestructive method for 3 D panicle modeling. The proposed method integrates shoot rice reconstruction with shape from silhouette, 2 D panicle segmentation with a deep convolutional neural network, and 3 D panicle segmentation with ray tracing and supervoxel clustering. A multiview imaging system was built to acquire image sequences of rice canopies with an efficiency of approximately 4 min per rice plant. The execution time of panicle modeling per rice plant using 90 images was approximately 26 min. The outputs of the algorithm for a single rice plant are a shoot rice model, surface shoot rice model, panicle model, and surface panicle model, all represented by a list of spatial coordinates. The efficiency and performance were evaluated and compared with the classical structure-from-motion algorithm. The results demonstrated that the proposed method is well qualified to recover the 3 D shapes of rice panicles from multiview images and is readily adaptable to rice plants of diverse accessions and growth stages. The proposed algorithm is superior to the structure-from-motion method in terms of texture preservation and computational efficiency. The sample images and implementation of the algorithm are available online. This automatic, cost-efficient, and nondestructive method of 3 D panicle modeling may be applied to high-throughput 3 D phenotyping of large rice populations.

True-color real-time imaging and spectroscopy of carbon nanotubes on substrates using enhanced Rayleigh scattering

Single-walled carbon nanotubes （SWCNTs） illuminated by white light should appear colored due to resonance Rayleigh scattering. However, true-color imaging of SWCNTs on substrates has not been reported, because of the extremely low scattering intensity of SWCNTs and the strong substrate scattering. Here we show that Rayleigh scattering can be greatly enhanced by the interface dipole enhancement effect. Consequently colorful SWCNTs on substrates can be directly imaged under an optical microscope by wide field supercontinuum laser illumination, which facilitates high throughput chirality assignment of individual SWCNTs. This approach, termed ＂Rayleigh imaging microscopy＂, is not restricted to SWCNTs, but widely applicable to a variety of nanomaterials, which enables the colorful nanoworld to be explored under optical microscopes.

Automatic marker-free registration of single tree point-cloud data based on rotating projection

Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry research.Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information.However,the placement of artificial reflectors in a forest with complex terrain for marker-based registration is time-consuming and difficult.In this study,an automatic coarse-to-fine method for the registration of pointcloud data from multiple scans of a single tree was proposed.In coarse registration,point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional(2D)images,which are used to estimate the initial positions of multiple scans.Corresponding feature-point pairs are then extracted from these series of 2D images.In fine registration,point-cloud data slicing and fitting methods are used to extract corresponding central stem and branch centers for use as tie points to calculate fine transformation parameters.To evaluate the accuracy of registration results,we propose a model of error evaluation via calculating the distances between center points from corresponding branches in adjacent scans.For accurate evaluation,we conducted experiments on two simulated trees and six real-world trees.Average registration errors of the proposed method were 0.026 m around on simulated tree point clouds,and 0.049 m around on real-world tree point clouds.

Three Dimensional Metal-Surface Processing Parameter Generation Through Machine Learning-Based Nonlinear Mapping

The accuracy and efficiency of three-dimensional(3D)surface forming,which directly affects the cycle and quality of production,is important in manufacturing.In practice,given the uncertainty of metal plate springback,an error exists between the actual plate and the target surface,which creates a nonlinear mapping from computer aided design models to bending surfaces.Technicians need to reconfigure parameters and process a surface multiple times to delicately control springback,which greatly wastes human and material resources.This study aims to address the springback control problem to improve the efficiency and accuracy of sheet metal forming.A basic computation approach is proposed based on the DeepFit model to calculate the springback value in 3D surface bending.To address the sample data shortage problem,we put forward an advanced approach by combining a deep learning model with case-based reasoning(CBR).Next,a multi-model fused bending parameter generation framework is devised to implement the advanced springback computation approach through surface data preprocessing,CBR-based model matching,convolution neural network-based machining surface generation,and bending parameter generation with a series of model transformations.Moreover,the proposed approaches and the framework are verified by considering saddle surface processing as an example.Overall,this study provides a new idea to improve the accuracy and efficiency of surface processing.

Endowing rotation invariance for 3D finger shape and vein verification

Finger vein biometrics have been extensively studied for the capability to detect aliveness,and the high security as intrinsic traits.However,vein pattern distortion caused by finger rotation degrades the performance of CNN in 2D finger vein recognition,especially in a contactless mode.To address the finger posture variation problem,we propose a 3D finger vein verification system extracting axial rotation invariant feature.An efficient 3D finger vein reconstruction optimization model is proposed and several accelerating strategies are adopted to achieve real-time 3D reconstruction on an embedded platform.The main contribution in this paper is that we are the first to propose a novel 3D point-cloud-based endto-end neural network to extract deep axial rotation invariant feature,namely 3DFVSNet.In the network,the rotation problem is transformed to a permutation problem with the help of specially designed rotation groups.Finally,to validate the performance of the proposed network more rigorously and enrich the database resources for the finger vein recognition community,we built the largest publicly available 3D finger vein dataset with different degrees of finger rotation,namely the Large-scale Finger Multi-Biometric Database-3D Pose Varied Finger Vein(SCUT LFMB-3DPVFV)Dataset.Experimental results on 3D finger vein datasets show that our 3DFVSNet holds strong robustness against axial rotation compared to other approaches.

A Comprehensive Method to Reject Detection Outliers by Combining Template Descriptor with Sparse 3D Point Clouds

We are using a template descriptor on the image in order to try and find the object. However, we have a sparse 3D point clouds of the world that is not used at all when looking for the object in the images. Considering there are many false alarms during the detection, we are interested in exploring how to combine the detections on the image with the 3D point clouds in order to reject some detection outliers. In this experiment we use semi-direct-monocular visual odometry (SVO) to provide 3D points coordinates and camera poses to project 3D points to 2D image coordinates. By un-projecting points in the tracking on the selection tree (TST) detection box back to 3D space, we can use 3D Gaussian ellipsoid fitting to determine object scales. By ruling out different scales of detected objects, we can reject most of the detection outliers of the object. © 2017, Shanghai Jiaotong University and Springer-Verlag Berlin Heidelberg.