Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportatio...Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.展开更多
Visible and near-infrared photodetectors are widely used in intelligent driving,health monitoring,and other fields.However,the application of photodetectors in the near-infrared region is significantly impacted by hig...Visible and near-infrared photodetectors are widely used in intelligent driving,health monitoring,and other fields.However,the application of photodetectors in the near-infrared region is significantly impacted by high dark current,which can greatly reduce their performance and sensitivity,thereby limiting their effectiveness in certain applications.In this work,the introduction of a C60 back interface layer successfully mitigated back interface reactions to decrease the thickness of the Mo(S,Se)_(2)layer,tailoring the back-contact barrier and preventing reverse charge injection,resulting in a kesterite photodetector with an ultralow dark current density of 5.2×10^(-9)mA/cm^(2)and ultra-weak-light detection at levels as low as 25 pW/cm^(2).Besides,under a self-powered operation,it demonstrates outstanding performance,achieving a peak responsivity of 0.68 A/W,a wide response range spanning from 300 to 1600 nm,and an impressive detectivity of 5.27×10^(14)Jones.In addition,it offers exceptionally rapid response times,with rise and decay times of 70 and 650 ns,respectively.This research offers important insights for developing high-performance self-powered near-infrared photodetectors that have high responsivity,rapid response times,and ultralow dark current.展开更多
To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transfo...To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.展开更多
BACKGROUND Liver transplantation has evolved into a safe life-saving operation and remains the golden standard in the treatment of end stage liver disease.The main limiting factor in the application of liver transplan...BACKGROUND Liver transplantation has evolved into a safe life-saving operation and remains the golden standard in the treatment of end stage liver disease.The main limiting factor in the application of liver transplantation is graft shortage.Many strategies have been developed in order to alleviate graft shortage,such as living donor partial liver transplantation and split liver transplantation for adult and pediatric patients.In these strategies,liver volume assessment is of paramount importance,as size mismatch can have severe consequences in the success of liver transplantation.AIM To evaluate the safety,feasibility,and accuracy of light detection and ranging(LIDAR)3D photography in the prediction of whole liver graft volume and mass.METHODS Seven liver grafts procured for orthotopic liver transplantation from brain deceased donors were prospectively measured with an LIDAR handheld camera and their mass was calculated and compared to their actual weight.RESULTS The mean error of all measurements was 17.03 g(range 3.56-59.33 g).Statistical analysis of the data yielded a Pearson correlation coefficient index of 0.9968,indicating a strong correlation between the values and a Student’s t-test P value of 0.26.Mean accuracy of the measurements was calculated at 97.88%.CONCLUSION Our preliminary data indicate that LIDAR scanning of liver grafts is a safe,cost-effective,and feasible method of ex vivo determination of whole liver volume and mass.More data are needed to determine the precision and accuracy of this method.展开更多
Chiral organic-inorganic hybrid halides show significant potential for applications in circularly polarized photodetection,chiral-induced spin selectivity effects(CISS),and nonlinear optics.However,the widespread use ...Chiral organic-inorganic hybrid halides show significant potential for applications in circularly polarized photodetection,chiral-induced spin selectivity effects(CISS),and nonlinear optics.However,the widespread use of toxic lead element poses environmental concerns,hindering the further applications.Herein,we synthesized a zero-dimensional(0D)lead-free chiral antimony-based halide(R/S-MBA)_(4)Sb_(2)Br_(10)with the coexistence of polarity and crystallographic chirality.The halides exhibit unique magneto-chiroptical effects due to the field-effect-induced fine-tuning of exciton energy,which is the first observation in chiral antimony-based halides.Furthermore,owing to its significant spontaneous polarization(5.0μC/cm^(2))and optical chirality(g_(CD)=0.0018),(R/S-MBA)_(4)Sb_(2)Br_(10)halide exhibits excellent performance in self-powered circularly polarized photodetection,nonlinear optics,and CISS effects.The self-powered photodetector demonstrates high sensitivity with distinguishable factors(g_(res)=0.53/-0.51@0 V)and broad spectral response.The single crystal(R/S-MBA)_(4)Sb_(2)Br_(10)also exhibits a high second-harmonic polarization response asymmetry factor(g_(SHG-CD)=0.98/-0.70)and strong second-harmonic generation intensity.These performances are among the best reported for chiral halides.Our research not only sheds new light on the investigation of magneto-chiroptical phenomena,but also marks a significant advancement in realizing high-sensitivity circularly polarized light detection within the realm of lead-free polar materials.展开更多
Coastal wetlands,crucial for global biodiversity and climate adaptation,provide essential ecosystem services such as carbon storage and flood protection.These vital areas are increasingly threatened by both natural an...Coastal wetlands,crucial for global biodiversity and climate adaptation,provide essential ecosystem services such as carbon storage and flood protection.These vital areas are increasingly threatened by both natural and human-induced changes,prompting the need for advanced monitoring techniques.This study employs unmanned aerial systems(UASs)equipped with light detection and ranging(LiDAR)and multispectral sensors to survey diverse wetland types across 8 sites in North Carolina.Utilizing high-resolution elevation data and detailed vegetation analysis,coupled with sophisticated machine learning algorithms,we achieved differentiated and highly precise classifications of wetland types.Classification accuracies varied by type,with estuarine intertidal emergent wetlands showing the highest classification accuracies due to less complex vegetation structure and clearer spectral signatures,especially when collections account for tidal influence.In contrast,palustrine forested and scrub-shrub wetlands presented lower accuracies,often due to the denser,mixed,and more complex vegetation structure and variable inundation levels,which complicate spectral differentiation and ground returns from LiDAR sensors.Overall,our integrated UAS-derived LiDAR and multispectral approach not only enhances the accuracy of wetland mapping but also offers a scalable,efficient,and cost-effective method that substantially advances conservation efforts and informs policy-making for coastal resilience.By demonstrating the usefulness of small-scale aerial data collection in ecological mapping,this study highlights the transformative potential of merging advanced technologies in environmental monitoring,underscoring their critical role in sustaining natural habitats and aiding in climate change mitigation strategies.展开更多
Rapid NIR light detection and/or writing has drawn much attention,but their practical applications have been limited by obtaining such NIR photodetectors.To address this problem,we have developed a simple and versatil...Rapid NIR light detection and/or writing has drawn much attention,but their practical applications have been limited by obtaining such NIR photodetectors.To address this problem,we have developed a simple and versatile strategy to prepare a non-woven fabric photodetector.The blue non-woven fabric photodetector has been prepared by coating photo-thermochromic ink(including crystal violet lactone(CVL)as the thermo-sensitive dye,polypyrrole(PPy)nanospheres as the photothermal component and hydroxyethyl cellulose(HEC)as the polymer matrix)on white non-woven fabric.When the blue fabric photodetector is irradiated by NIR(808-nm as model,0.75 W cm^(−2))laser,the discoloration occurs in 35 s,and higher laser intensity confers more rapid discoloration.This discoloration results from the photothermal effect of PPy which confers the elevation of temperature(>50℃)and then converts CVL to its leuco form(colorless).When the laser is turned off,the temperature drops to below the transition temperature(<43℃),and then CVL reverts to its initial blue color.Moreover,different figures and images can be easily printed on the fabric photodetector by 808 nm laser,and then they can be erased automatically under ambient conditions,with excellent cycling stability.Therefore,this fabric photodetector may act as a new platform for rapid NIR light detection and writing.展开更多
Traffic light detection and recognition is essential for autonomous driving in urban environments. A camera based algorithm for real-time robust traffic light detection and recognition was proposed, and especially des...Traffic light detection and recognition is essential for autonomous driving in urban environments. A camera based algorithm for real-time robust traffic light detection and recognition was proposed, and especially designed for autonomous vehicles. Although the current reliable traffic light recognition algorithms operate well under way, most of them are mainly designed for detection at a fixed position and the effect on autonomous vehicles under real-world conditions is still limited. Some methods achieve high accuracy on autonomous vehicle, but they can＇t work normally without the aid of high-precision priori map. The authors presented a camera-based algorithm for the problem. The image processing flow can be divided into three steps, including pre-processing, detection and recognition. Firstly, red-green-blue （RGB） color space is converted to hue-saturation-value （HSV） as main content of pre-processing. In detection step, the transcendental color threshold method is used for initial filterings, meanwhile, the prior knowledge is performed to scan the scene in order to quickly establish candidate regions. For recognition, this article use histogram of oriented gradients （HOG） features and support vector machine （SVM） as well to recognize the state of traffic light. The proposed system on our autonomous vehicle was evaluated. With voting schemes, the proposed can provide a sufficient accuracy for autonomous vehicles in urban enviroment.展开更多
2D Ruddlesden-Popper(RP)polar perovskite,displaying the intrinsic optical anisotropy and structural polarity,has a fantastic application perspective in self-powered polarized light detection.However,the weak van der W...2D Ruddlesden-Popper(RP)polar perovskite,displaying the intrinsic optical anisotropy and structural polarity,has a fantastic application perspective in self-powered polarized light detection.However,the weak van der Waals interaction between the organic spacing bilayers is insufficient to preserve the stability of RP-type materials.Hence,it is of great significance to explore new stable 2D RP-phase candidates.In this work,we have successfully constructed a highly-stable polar 2D perovskite,(t-ACH)_(2)PbI_(4)(1,where t-ACH^(+)is HOOC_(8)H_(12)NH_(3)^(+)),by adopting a hydrophobic carboxylate trans-isomer of tranexamic acid as the spacing component.Strikingly,strong O-H…O hydrogen bonds between t-ACH^(+)organic bilayers compose the dimer,thus decreasing van der Waals gap and enhancing structural stability.Besides,such orientational hydrogen bonds contribute to the formation of structural polarity and generate an obvious bulk photovoltaic effect in 1,which facilitates its self-powered photodetection.As predicted,the combination of inherent anisotropy and polarity leads to self-powered polarized-light detection with a high ratio of around∼5.3,superior to those of inorganic 2D counterparts.This work paves a potential way to design highly-stable 2D perovskites for high-performance optoelectronic devices.展开更多
Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribu...Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribution along with multiple reconstruction methods for super-resolution imaging.The proposed technology reduces the point-spread function of an imag-ing system through single-neutron detection and event reconstruction,thereby significantly improving imaging resolution.A single-neutron detection experiment was conducted using a highly practical and efficient^(6)LiF-ZnS scintillation screen of a cold neutron imaging device in the research reactor.In milliseconds of exposure time,a large number of weak light clusters and their distribution in the scintillation screen were recorded frame by frame,to complete single-neutron detection.Several reconstruction algorithms were proposed for the calculations.The location of neutron capture was calculated using several processing methods such as noise removal,filtering,spot segmentation,contour analysis,and local positioning.The proposed algorithm achieved a higher imaging resolution and faster reconstruction speed,and single-neutron super-resolution imaging was realized by combining single-neutron detection experiments and reconstruction calculations.The results show that the resolution of the 100μm thick^(6)LiF-ZnS scintillation screen can be improved from 125 to 40 microns.This indicates that the proposed single-neutron detection and calculation method is effective and can significantly improve imaging resolution.展开更多
We propose optical experiments to study the depth of field for a thermal light lensless ghost imaging system. It is proved that the diaphragm is an important factor to influence the depth of field, and the ghost image...We propose optical experiments to study the depth of field for a thermal light lensless ghost imaging system. It is proved that the diaphragm is an important factor to influence the depth of field, and the ghost images of two detected objects with longitudinal distance less than the depth of field can be achieved simultaneously. The longitudinal coherence scale of the thermal light lensless ghost imaging determines the depth of field. Theoretical analysis can well explain the experimental results.展开更多
Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,w...Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.展开更多
We theoretically study the spin transport through a two-terminal quantum dot device under the influence of a symmetric spin bias and circularly polarized light. It is found that the combination of the circularly polar...We theoretically study the spin transport through a two-terminal quantum dot device under the influence of a symmetric spin bias and circularly polarized light. It is found that the combination of the circularly polarized light and the applied spin bias can result in a net charge current. The resultant charge current is large enough to be measured when properly choosing the system parameters. The resultant charge current can be used to deduce the spin bias due to the fact that there exists a simple linear relation between them. When the external circuit is open, a charge bias instead of a charge current can be induced, which is also measurable by present technologies. These findings indicate a new approach to detect the spin bias by using circularly polarized light.展开更多
Unmanned aerial vehicle laser scanning(ULS)and terrestrial laser scanning(TLS)systems are effective ways to capture forest structures from top and side views,respectively.The registration of TLS and ULS data is a prer...Unmanned aerial vehicle laser scanning(ULS)and terrestrial laser scanning(TLS)systems are effective ways to capture forest structures from top and side views,respectively.The registration of TLS and ULS data is a prerequisite for a comprehensive forest structure representation.Conventional registration methods based on geometric features(e.g.,points,lines,and planes)are likely to fail due to the irregular natural point distributions of forest point clouds.Currently,automatic registration methods for forest point clouds typically rely on tree attributes(such as tree position and stem diameter).However,these methods are often unsuitable for forests with diverse compositions,complex terrains,irregular tree layouts,and insufficient common trees.In this study,an automated method is proposed to register ULS and TLS forest point clouds using ground points as registration primitives,which operates independently of tree attribute extraction and is estimated to reduce processing time by over 50%.A new evaluation method for registration accuracy evaluation is proposed,where transformation parameters from each TLS scan to the ULS obtained by the proposed registration algorithm are used to derive transformation parameters between TLS scans,which are then compared to reference parameters obtained using artificial spherical targets.Conventional ULS-TLS registration evaluation methods mostly rely on the manual corresponding points selection that is subject to inherent subjective errors,or control points in both TLS and ULS data that are difficult to collect.The proposed method presents an objective and accurate solution for ULS-TLS registration accuracy evaluation that effectively eliminates these limitations.The proposed method was tested on 12 plots with diverse stem densities,tree species,and altitudes located in a mountain forest.A total of 124 TLS scans were successfully registered to ULS data.The registration accuracy was assessed using both the conventional evaluation method and the proposed new evaluation method,with average rotation errors of 2.03 and 2.06 mrad,and average translation errors of 7.63 and 6.51 cm,respectively.The registration accuracies demonstrate that the proposed algorithm effectively and accurately registers TLS to ULS point clouds.展开更多
Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically ...Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar(InSAR),fail to accurately capture understory terrain due to limited penetration capabilities,resulting in elevation overestimation in densely vegetated areas.While airborne light detection and ranging(LiDAR)can provide high-accuracy DTMs,its limited spatial coverage and high acquisition cost hinder large-scale applications.Thus,there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models(DSMs).In this study,we propose a simple,interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation(GEDI)and Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)to construct a tree height surface model,which is then subtracted from the stereo-derived DSM to generate the final DTM.By directly incorporating LiDAR constraints,the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs,ensuring ease of implementation and broad applicability.In contrast to machine learning-based terrain modeling methods,which are often prone to overfitting and data bias,the proposed approach is simple,interpretable,and robust across diverse forested landscapes.The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model(DEM)and the forest and buildings removed DEM(FABDEM),a global bare-earth elevation model corrected for vegetation bias.The results indicate that the proposed DTM consistently outperforms the Copernicus DEM(CopDEM)and achieves accuracy comparable to FABDEM.In addition,its finer spatial resolution of 1 m,compared to the 30 m resolution of FABDEM,allows for more detailed terrain representation and better capture of fine-scale variation.This advantage is most pronounced in gently to moderately sloped areas,where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM.The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints,offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable.To illustrate the potential utility of the proposed DTM,we applied it to a fire risk mapping application based on topographic parameters such as slope,aspect,and elevation.This case highlights how improved terrain representation can support geospatial hazard assessments.展开更多
This study investigated the effects of polarized and spectral light interactions on locust polarotaxic behavior and elucidated the regulatory mechanisms of polarized and spectral lights.Locust visual response effect w...This study investigated the effects of polarized and spectral light interactions on locust polarotaxic behavior and elucidated the regulatory mechanisms of polarized and spectral lights.Locust visual response effect was investigated using a combined light source system comprising linear detection polarization violet light with various spectrum lights and a response device to explore the interaction mechanism of polarized and spectral lights on locust visual sensitivity characteristics and the specific sensitivity of locust phototaxis and polartaxis.Results indicated that the polarized vector sensitivity of locusts was related to combined light intensity,showing high visual response sensitivity at 0°and 180°under 1000 lx,whereas under rated illumination(150 mW/cm^(2)),the coupled spectrum attributes induced changes in the locusts’sensitive vectors.UV,violet,and blue lights enhanced the sensitivity at 90°and 270°,and green and orange lights did so at 0°and 180°.Moreover,UV and violet lights enhanced the aggregation and trend sensitivity at 210°and 30°,blue,green and orange lights induced high sensitivity at 0°and 180°.Under increasing illumination,the enhanced effect of light intensity on aggregation sensitivity under blue,green,and orange spectra and on trend sensitivity under orange spectra at 90°and 270°were highly pronounced because of the interaction between heterogeneous spectrum illumination and linear detection polarization vector illumination.Meanwhile,the spectral attribute determined the locust visual response effect,which was affected by the linear detection polarization vector.When illumination increased to rated illumination,coupled light intensity induced a specific vector sensitivity related to optical distance,showing the strongest response sensitivity to 180°under orange spectra and the strongest aggregation and trend sensitivity to 210°under violet spectra due to the interplay of polarization degree,coupling light intensity,and specific vision sensitivity caused by partially polarized light.Then,the locust visual response effect was improved by utilizing the enhancement effect of polarized violet light coupled with violet light at a close range and the inductive effect of polarized violet light coupled with orange light at a long distance,which provide theoretical support for understanding locust polarotactic orientation mechanisms,facilitate the development of polarization induced light sources for attracting locusts.展开更多
数字航空影像和机载点云之间的配准参数精度会直接影响到配准效果,利用共线方程及影像特征点和点云特征点之间计算相似性测度的方法进行参数优化,有效避免了由于初始参数误差导致的配准偏差。首先,提取航空影像及激光雷达(light detecti...数字航空影像和机载点云之间的配准参数精度会直接影响到配准效果,利用共线方程及影像特征点和点云特征点之间计算相似性测度的方法进行参数优化,有效避免了由于初始参数误差导致的配准偏差。首先,提取航空影像及激光雷达(light detection and ranging,LiDAR)点云的特征点;然后,根据初始配准参数及距离误差计算影像与点云之间的匹配点对;最后,通过强制搜索(brute-force,BF)优化方法来寻找更加精确的匹配参数。此外,还构建了2D-3D对应区域的数据集,用于航空影像和机载LiDAR数据配准的相关研究。展开更多
Background: The distribution of forest vegetation within urban environments is critically important as it influences urban environmental conditions and the energy exchange through the absorption of solar radiation and...Background: The distribution of forest vegetation within urban environments is critically important as it influences urban environmental conditions and the energy exchange through the absorption of solar radiation and modulation of evapotranspiration. It also plays an important role filtering urban water systems and reducing storm water runoff.Methods: We investigate the capacity of ALS data to individually detect, map and characterize large(taller than15 m) trees within the City of Vancouver. Large trees are critical for the function and character of Vancouver’s urban forest. We used an object-based approach for individual tree detection and segmentation to determine tree locations(position of the stem), to delineate the shape of the crowns and to categorize the latter either as coniferous or deciduous.Results: Results indicate a detection rate of 76.6% for trees > 15 m with a positioning error of 2.11 m(stem location). Extracted tree heights possessed a RMSE of 2.60 m and a bias of-1.87 m, whereas crown diameter was derived with a RMSE of 3.85 m and a bias of-2.06 m. Missed trees are principally a result of undetected treetops occurring in dense, overlapping canopies with more accurate detection and delineation of trees in open areas.Conclusion: By identifying key structural trees across Vancouver’s urban forests, we can better understand their role in providing ecosystem goods and services for city residents.展开更多
In recent years,propelled by the rapid iterative advancements in digital imaging technology and the semiconductor industry,encompassing microelectronic design,manufacturing,packaging,and testing,time-of-flight(ToF)-ba...In recent years,propelled by the rapid iterative advancements in digital imaging technology and the semiconductor industry,encompassing microelectronic design,manufacturing,packaging,and testing,time-of-flight(ToF)-based imaging systems for acquiring depth information have garnered considerable attention from both academia and industry.This technology has emerged as a focal point of research within the realm of 3D imaging.Owing to its relatively straightforward principles and exceptional performance,ToF technology finds extensive applications across various domains including human−computer interaction,autonomous driving,industrial inspection,medical and healthcare,augmented reality,smart homes,and 3D reconstruction,among others.Notably,the increasing maturity of ToF-based LiDAR systems is evident in current developments.This paper comprehensively reviews the fundamental principles of ToF technology and LiDAR systems,alongside recent research advancements.It elucidates the innovative aspects and technical challenges encountered in both transmitter(TX)and receiver(RX),providing detailed discussions on corresponding solutions.Furthermore,the paper explores prospective avenues for future research,offering valuable insights for subsequent investigations.展开更多
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).
文摘Transportation systems are experiencing a significant transformation due to the integration of advanced technologies, including artificial intelligence and machine learning. In the context of intelligent transportation systems (ITS) and Advanced Driver Assistance Systems (ADAS), the development of efficient and reliable traffic light detection mechanisms is crucial for enhancing road safety and traffic management. This paper presents an optimized convolutional neural network (CNN) framework designed to detect traffic lights in real-time within complex urban environments. Leveraging multi-scale pyramid feature maps, the proposed model addresses key challenges such as the detection of small, occluded, and low-resolution traffic lights amidst complex backgrounds. The integration of dilated convolutions, Region of Interest (ROI) alignment, and Soft Non-Maximum Suppression (Soft-NMS) further improves detection accuracy and reduces false positives. By optimizing computational efficiency and parameter complexity, the framework is designed to operate seamlessly on embedded systems, ensuring robust performance in real-world applications. Extensive experiments using real-world datasets demonstrate that our model significantly outperforms existing methods, providing a scalable solution for ITS and ADAS applications. This research contributes to the advancement of Artificial Intelligence-driven (AI-driven) pattern recognition in transportation systems and offers a mathematical approach to improving efficiency and safety in logistics and transportation networks.
基金supported by the National Natural Science Foundation of China(No.52472225)the Science and Technology Plan Project of Shenzhen(No.20220808165025003),China。
文摘Visible and near-infrared photodetectors are widely used in intelligent driving,health monitoring,and other fields.However,the application of photodetectors in the near-infrared region is significantly impacted by high dark current,which can greatly reduce their performance and sensitivity,thereby limiting their effectiveness in certain applications.In this work,the introduction of a C60 back interface layer successfully mitigated back interface reactions to decrease the thickness of the Mo(S,Se)_(2)layer,tailoring the back-contact barrier and preventing reverse charge injection,resulting in a kesterite photodetector with an ultralow dark current density of 5.2×10^(-9)mA/cm^(2)and ultra-weak-light detection at levels as low as 25 pW/cm^(2).Besides,under a self-powered operation,it demonstrates outstanding performance,achieving a peak responsivity of 0.68 A/W,a wide response range spanning from 300 to 1600 nm,and an impressive detectivity of 5.27×10^(14)Jones.In addition,it offers exceptionally rapid response times,with rise and decay times of 70 and 650 ns,respectively.This research offers important insights for developing high-performance self-powered near-infrared photodetectors that have high responsivity,rapid response times,and ultralow dark current.
基金The Cultivation Fund of the Key Scientific and Technical Innovation Project of Higher Education of Ministry of Education (No.705020)
文摘To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.
基金the European Union and Greek national funds through the Operational Program Competitiveness,Entrepreneurship and Innovation,No.T1EDK-03599.
文摘BACKGROUND Liver transplantation has evolved into a safe life-saving operation and remains the golden standard in the treatment of end stage liver disease.The main limiting factor in the application of liver transplantation is graft shortage.Many strategies have been developed in order to alleviate graft shortage,such as living donor partial liver transplantation and split liver transplantation for adult and pediatric patients.In these strategies,liver volume assessment is of paramount importance,as size mismatch can have severe consequences in the success of liver transplantation.AIM To evaluate the safety,feasibility,and accuracy of light detection and ranging(LIDAR)3D photography in the prediction of whole liver graft volume and mass.METHODS Seven liver grafts procured for orthotopic liver transplantation from brain deceased donors were prospectively measured with an LIDAR handheld camera and their mass was calculated and compared to their actual weight.RESULTS The mean error of all measurements was 17.03 g(range 3.56-59.33 g).Statistical analysis of the data yielded a Pearson correlation coefficient index of 0.9968,indicating a strong correlation between the values and a Student’s t-test P value of 0.26.Mean accuracy of the measurements was calculated at 97.88%.CONCLUSION Our preliminary data indicate that LIDAR scanning of liver grafts is a safe,cost-effective,and feasible method of ex vivo determination of whole liver volume and mass.More data are needed to determine the precision and accuracy of this method.
基金supported by the Guangxi Natural Science Foundation(Nos.2025GXNSFDA069038)the Special Fund for Science and Technology Development of Guangxi(No.AD25069078)+4 种基金the Guangxi Natural Science Foundation(No.AA23073018)the National Natural Science Foundation of China(Nos.22175043 and 52162021)the Open Foundation of State Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite Structures(No.MMCS2023OF05)the Innovation Project of Guangxi Graduate Education(No.YCBZ2025046)the Innovation Project of Guangxi Graduate Education(No.YCSW2025092).
文摘Chiral organic-inorganic hybrid halides show significant potential for applications in circularly polarized photodetection,chiral-induced spin selectivity effects(CISS),and nonlinear optics.However,the widespread use of toxic lead element poses environmental concerns,hindering the further applications.Herein,we synthesized a zero-dimensional(0D)lead-free chiral antimony-based halide(R/S-MBA)_(4)Sb_(2)Br_(10)with the coexistence of polarity and crystallographic chirality.The halides exhibit unique magneto-chiroptical effects due to the field-effect-induced fine-tuning of exciton energy,which is the first observation in chiral antimony-based halides.Furthermore,owing to its significant spontaneous polarization(5.0μC/cm^(2))and optical chirality(g_(CD)=0.0018),(R/S-MBA)_(4)Sb_(2)Br_(10)halide exhibits excellent performance in self-powered circularly polarized photodetection,nonlinear optics,and CISS effects.The self-powered photodetector demonstrates high sensitivity with distinguishable factors(g_(res)=0.53/-0.51@0 V)and broad spectral response.The single crystal(R/S-MBA)_(4)Sb_(2)Br_(10)also exhibits a high second-harmonic polarization response asymmetry factor(g_(SHG-CD)=0.98/-0.70)and strong second-harmonic generation intensity.These performances are among the best reported for chiral halides.Our research not only sheds new light on the investigation of magneto-chiroptical phenomena,but also marks a significant advancement in realizing high-sensitivity circularly polarized light detection within the realm of lead-free polar materials.
基金North Carolina Department of Transportation(NCDOT),contract number RP 2020-04,awarded to N.G.P.(lead principal investigator)J.N.H.in the Department of Earth and Ocean Sciences at the University of North Carolina Wilmington.
文摘Coastal wetlands,crucial for global biodiversity and climate adaptation,provide essential ecosystem services such as carbon storage and flood protection.These vital areas are increasingly threatened by both natural and human-induced changes,prompting the need for advanced monitoring techniques.This study employs unmanned aerial systems(UASs)equipped with light detection and ranging(LiDAR)and multispectral sensors to survey diverse wetland types across 8 sites in North Carolina.Utilizing high-resolution elevation data and detailed vegetation analysis,coupled with sophisticated machine learning algorithms,we achieved differentiated and highly precise classifications of wetland types.Classification accuracies varied by type,with estuarine intertidal emergent wetlands showing the highest classification accuracies due to less complex vegetation structure and clearer spectral signatures,especially when collections account for tidal influence.In contrast,palustrine forested and scrub-shrub wetlands presented lower accuracies,often due to the denser,mixed,and more complex vegetation structure and variable inundation levels,which complicate spectral differentiation and ground returns from LiDAR sensors.Overall,our integrated UAS-derived LiDAR and multispectral approach not only enhances the accuracy of wetland mapping but also offers a scalable,efficient,and cost-effective method that substantially advances conservation efforts and informs policy-making for coastal resilience.By demonstrating the usefulness of small-scale aerial data collection in ecological mapping,this study highlights the transformative potential of merging advanced technologies in environmental monitoring,underscoring their critical role in sustaining natural habitats and aiding in climate change mitigation strategies.
基金This work was financially supported by the National Natural Science Foundation of China(51773036 and 51972056)Shanghai Shuguang Program(18SG29)+3 种基金Natural Science Foundation of Shanghai(18ZR1401700)Innovation Program of Shanghai Municipal Education Commission(2017-01-07-00-03-E00055)the Fundamental Research Funds for the Central UniversitiesDHU Distinguished Young Professor Program.
文摘Rapid NIR light detection and/or writing has drawn much attention,but their practical applications have been limited by obtaining such NIR photodetectors.To address this problem,we have developed a simple and versatile strategy to prepare a non-woven fabric photodetector.The blue non-woven fabric photodetector has been prepared by coating photo-thermochromic ink(including crystal violet lactone(CVL)as the thermo-sensitive dye,polypyrrole(PPy)nanospheres as the photothermal component and hydroxyethyl cellulose(HEC)as the polymer matrix)on white non-woven fabric.When the blue fabric photodetector is irradiated by NIR(808-nm as model,0.75 W cm^(−2))laser,the discoloration occurs in 35 s,and higher laser intensity confers more rapid discoloration.This discoloration results from the photothermal effect of PPy which confers the elevation of temperature(>50℃)and then converts CVL to its leuco form(colorless).When the laser is turned off,the temperature drops to below the transition temperature(<43℃),and then CVL reverts to its initial blue color.Moreover,different figures and images can be easily printed on the fabric photodetector by 808 nm laser,and then they can be erased automatically under ambient conditions,with excellent cycling stability.Therefore,this fabric photodetector may act as a new platform for rapid NIR light detection and writing.
基金supported by Natural Basic Research Program of China (91120306, 61203366)
文摘Traffic light detection and recognition is essential for autonomous driving in urban environments. A camera based algorithm for real-time robust traffic light detection and recognition was proposed, and especially designed for autonomous vehicles. Although the current reliable traffic light recognition algorithms operate well under way, most of them are mainly designed for detection at a fixed position and the effect on autonomous vehicles under real-world conditions is still limited. Some methods achieve high accuracy on autonomous vehicle, but they can＇t work normally without the aid of high-precision priori map. The authors presented a camera-based algorithm for the problem. The image processing flow can be divided into three steps, including pre-processing, detection and recognition. Firstly, red-green-blue （RGB） color space is converted to hue-saturation-value （HSV） as main content of pre-processing. In detection step, the transcendental color threshold method is used for initial filterings, meanwhile, the prior knowledge is performed to scan the scene in order to quickly establish candidate regions. For recognition, this article use histogram of oriented gradients （HOG） features and support vector machine （SVM） as well to recognize the state of traffic light. The proposed system on our autonomous vehicle was evaluated. With voting schemes, the proposed can provide a sufficient accuracy for autonomous vehicles in urban enviroment.
基金supported by the National Natural Science Foundation of China(NSFC,Nos.22125110,U23A2094,22205233,22193042,21921001,22305248 and U21A2069)the Natural Science Foundation of Fujian Province(No.2023J02028)+3 种基金the Key Research Program of Frontier Sciences of Chinese Academy of Sciences(No.ZDBS-LY-SLH024)Fujian Science&Technology Innovation Laboratory for Optoelectronic Information of China(No.2021ZR126)the National Key Research and Development Program of China(No.2019YFA0210402)the China Postdoctoral Science Foundation(Nos.2022TQ0337 and 2023M733497).
文摘2D Ruddlesden-Popper(RP)polar perovskite,displaying the intrinsic optical anisotropy and structural polarity,has a fantastic application perspective in self-powered polarized light detection.However,the weak van der Waals interaction between the organic spacing bilayers is insufficient to preserve the stability of RP-type materials.Hence,it is of great significance to explore new stable 2D RP-phase candidates.In this work,we have successfully constructed a highly-stable polar 2D perovskite,(t-ACH)_(2)PbI_(4)(1,where t-ACH^(+)is HOOC_(8)H_(12)NH_(3)^(+)),by adopting a hydrophobic carboxylate trans-isomer of tranexamic acid as the spacing component.Strikingly,strong O-H…O hydrogen bonds between t-ACH^(+)organic bilayers compose the dimer,thus decreasing van der Waals gap and enhancing structural stability.Besides,such orientational hydrogen bonds contribute to the formation of structural polarity and generate an obvious bulk photovoltaic effect in 1,which facilitates its self-powered photodetection.As predicted,the combination of inherent anisotropy and polarity leads to self-powered polarized-light detection with a high ratio of around∼5.3,superior to those of inorganic 2D counterparts.This work paves a potential way to design highly-stable 2D perovskites for high-performance optoelectronic devices.
基金supported by the National Natural Science Foundation of China(Nos.12205271,12075217,U20B2011,and 51978218)Sichuan Science and Technology Program(No.2019ZDZX0010)the National Key R&D Program of China(No.2022YFA1604002).
文摘Neutron capture event imaging is a novel technique that has the potential to substantially enhance the resolution of existing imaging systems.This study provides a measurement method for neutron capture event distribution along with multiple reconstruction methods for super-resolution imaging.The proposed technology reduces the point-spread function of an imag-ing system through single-neutron detection and event reconstruction,thereby significantly improving imaging resolution.A single-neutron detection experiment was conducted using a highly practical and efficient^(6)LiF-ZnS scintillation screen of a cold neutron imaging device in the research reactor.In milliseconds of exposure time,a large number of weak light clusters and their distribution in the scintillation screen were recorded frame by frame,to complete single-neutron detection.Several reconstruction algorithms were proposed for the calculations.The location of neutron capture was calculated using several processing methods such as noise removal,filtering,spot segmentation,contour analysis,and local positioning.The proposed algorithm achieved a higher imaging resolution and faster reconstruction speed,and single-neutron super-resolution imaging was realized by combining single-neutron detection experiments and reconstruction calculations.The results show that the resolution of the 100μm thick^(6)LiF-ZnS scintillation screen can be improved from 125 to 40 microns.This indicates that the proposed single-neutron detection and calculation method is effective and can significantly improve imaging resolution.
基金Supported by the Beijing Natural Science Foundation under Grant No 4133086the Fundamental Research Funds for th Central Universities under Grant No 2-9-2014-022
文摘We propose optical experiments to study the depth of field for a thermal light lensless ghost imaging system. It is proved that the diaphragm is an important factor to influence the depth of field, and the ghost images of two detected objects with longitudinal distance less than the depth of field can be achieved simultaneously. The longitudinal coherence scale of the thermal light lensless ghost imaging determines the depth of field. Theoretical analysis can well explain the experimental results.
基金supported by a grant from the National Key Research and Development Project(2023YFB4302100)Key Research and Development Project of Jiangxi Province(No.20232ACE01011)Independent Deployment Project of Ganjiang Innovation Research Institute,Chinese Academy of Sciences(E255J001).
文摘Aiming at the limitations of the existing railway foreign object detection methods based on two-dimensional(2D)images,such as short detection distance,strong influence of environment and lack of distance information,we propose Rail-PillarNet,a three-dimensional(3D)LIDAR(Light Detection and Ranging)railway foreign object detection method based on the improvement of PointPillars.Firstly,the parallel attention pillar encoder(PAPE)is designed to fully extract the features of the pillars and alleviate the problem of local fine-grained information loss in PointPillars pillars encoder.Secondly,a fine backbone network is designed to improve the feature extraction capability of the network by combining the coding characteristics of LIDAR point cloud feature and residual structure.Finally,the initial weight parameters of the model were optimised by the transfer learning training method to further improve accuracy.The experimental results on the OSDaR23 dataset show that the average accuracy of Rail-PillarNet reaches 58.51%,which is higher than most mainstream models,and the number of parameters is 5.49 M.Compared with PointPillars,the accuracy of each target is improved by 10.94%,3.53%,16.96%and 19.90%,respectively,and the number of parameters only increases by 0.64M,which achieves a balance between the number of parameters and accuracy.
基金Supported by the National Natural Science Foundation of China under Grant No 11404142the Youth Teacher Foundation of Huaiyin Institute of Technology under Grant No 2717577
文摘We theoretically study the spin transport through a two-terminal quantum dot device under the influence of a symmetric spin bias and circularly polarized light. It is found that the combination of the circularly polarized light and the applied spin bias can result in a net charge current. The resultant charge current is large enough to be measured when properly choosing the system parameters. The resultant charge current can be used to deduce the spin bias due to the fact that there exists a simple linear relation between them. When the external circuit is open, a charge bias instead of a charge current can be induced, which is also measurable by present technologies. These findings indicate a new approach to detect the spin bias by using circularly polarized light.
基金supported partially by the National Key Research and Development Program of China(No.2023YFF1303901)the National Natural Science Foundation of China(Nos.32171789,12411530088,and 32371654)the Joint Open Funded Project of State Key Laboratory of Geo-Information Engineering and Key Laboratory of the Ministry of Natural Resources for Surveying and Mapping Science and Geo-spatial Information Technology(No.2022-02-02).
文摘Unmanned aerial vehicle laser scanning(ULS)and terrestrial laser scanning(TLS)systems are effective ways to capture forest structures from top and side views,respectively.The registration of TLS and ULS data is a prerequisite for a comprehensive forest structure representation.Conventional registration methods based on geometric features(e.g.,points,lines,and planes)are likely to fail due to the irregular natural point distributions of forest point clouds.Currently,automatic registration methods for forest point clouds typically rely on tree attributes(such as tree position and stem diameter).However,these methods are often unsuitable for forests with diverse compositions,complex terrains,irregular tree layouts,and insufficient common trees.In this study,an automated method is proposed to register ULS and TLS forest point clouds using ground points as registration primitives,which operates independently of tree attribute extraction and is estimated to reduce processing time by over 50%.A new evaluation method for registration accuracy evaluation is proposed,where transformation parameters from each TLS scan to the ULS obtained by the proposed registration algorithm are used to derive transformation parameters between TLS scans,which are then compared to reference parameters obtained using artificial spherical targets.Conventional ULS-TLS registration evaluation methods mostly rely on the manual corresponding points selection that is subject to inherent subjective errors,or control points in both TLS and ULS data that are difficult to collect.The proposed method presents an objective and accurate solution for ULS-TLS registration accuracy evaluation that effectively eliminates these limitations.The proposed method was tested on 12 plots with diverse stem densities,tree species,and altitudes located in a mountain forest.A total of 124 TLS scans were successfully registered to ULS data.The registration accuracy was assessed using both the conventional evaluation method and the proposed new evaluation method,with average rotation errors of 2.03 and 2.06 mrad,and average translation errors of 7.63 and 6.51 cm,respectively.The registration accuracies demonstrate that the proposed algorithm effectively and accurately registers TLS to ULS point clouds.
基金supported by the National Key Research and Development Program of China(Nos.SQ2022YFB3900026 and 2022YFB3903305)supported by the Leading Talents of Guangdong Pearl River Talent Program(No.2021CX02S024)the Guangdong S&T programme(No.2024B1212050011).
文摘Accurate digital terrain models(DTMs)are essential for a wide range of geospatial and environmental applications,yet their derivation in forested regions remains a significant challenge.Existing global DTMs,typically generated from satellite stereo photogrammetry or interferometric synthetic aperture radar(InSAR),fail to accurately capture understory terrain due to limited penetration capabilities,resulting in elevation overestimation in densely vegetated areas.While airborne light detection and ranging(LiDAR)can provide high-accuracy DTMs,its limited spatial coverage and high acquisition cost hinder large-scale applications.Thus,there is an urgent need for a scalable and cost-effective approach to extract DTMs directly from satellite-derived digital surface models(DSMs).In this study,we propose a simple,interpretable understory terrain extraction method that utilizes canopy height data from Global Ecosystem Dynamics Investigation(GEDI)and Ice,Cloud,and Land Elevation Satellite-2(ICESat-2)to construct a tree height surface model,which is then subtracted from the stereo-derived DSM to generate the final DTM.By directly incorporating LiDAR constraints,the method avoids error propagation from multiple heterogeneous datasets and reduces reliance on ancillary inputs,ensuring ease of implementation and broad applicability.In contrast to machine learning-based terrain modeling methods,which are often prone to overfitting and data bias,the proposed approach is simple,interpretable,and robust across diverse forested landscapes.The accuracy of the resulting DTM was validated against airborne LiDAR reference data and compared with both the Copernicus Digital Elevation Model(DEM)and the forest and buildings removed DEM(FABDEM),a global bare-earth elevation model corrected for vegetation bias.The results indicate that the proposed DTM consistently outperforms the Copernicus DEM(CopDEM)and achieves accuracy comparable to FABDEM.In addition,its finer spatial resolution of 1 m,compared to the 30 m resolution of FABDEM,allows for more detailed terrain representation and better capture of fine-scale variation.This advantage is most pronounced in gently to moderately sloped areas,where the proposed DTM shows clearly higher accuracy than both the CopDEM and FABDEM.The results confirm that high-resolution DTMs can be effectively extracted from DSMs using spaceborne LiDAR constraints,offering a scalable solution for terrain modeling in forested environments where airborne LiDAR is unavailable.To illustrate the potential utility of the proposed DTM,we applied it to a fire risk mapping application based on topographic parameters such as slope,aspect,and elevation.This case highlights how improved terrain representation can support geospatial hazard assessments.
基金supported by the Scientific and Technological Project of Henan Province,China(Grant No.242102111179)the Natural Science Foundation Project of Henan Province,China(Grant No.232300420024).
文摘This study investigated the effects of polarized and spectral light interactions on locust polarotaxic behavior and elucidated the regulatory mechanisms of polarized and spectral lights.Locust visual response effect was investigated using a combined light source system comprising linear detection polarization violet light with various spectrum lights and a response device to explore the interaction mechanism of polarized and spectral lights on locust visual sensitivity characteristics and the specific sensitivity of locust phototaxis and polartaxis.Results indicated that the polarized vector sensitivity of locusts was related to combined light intensity,showing high visual response sensitivity at 0°and 180°under 1000 lx,whereas under rated illumination(150 mW/cm^(2)),the coupled spectrum attributes induced changes in the locusts’sensitive vectors.UV,violet,and blue lights enhanced the sensitivity at 90°and 270°,and green and orange lights did so at 0°and 180°.Moreover,UV and violet lights enhanced the aggregation and trend sensitivity at 210°and 30°,blue,green and orange lights induced high sensitivity at 0°and 180°.Under increasing illumination,the enhanced effect of light intensity on aggregation sensitivity under blue,green,and orange spectra and on trend sensitivity under orange spectra at 90°and 270°were highly pronounced because of the interaction between heterogeneous spectrum illumination and linear detection polarization vector illumination.Meanwhile,the spectral attribute determined the locust visual response effect,which was affected by the linear detection polarization vector.When illumination increased to rated illumination,coupled light intensity induced a specific vector sensitivity related to optical distance,showing the strongest response sensitivity to 180°under orange spectra and the strongest aggregation and trend sensitivity to 210°under violet spectra due to the interplay of polarization degree,coupling light intensity,and specific vision sensitivity caused by partially polarized light.Then,the locust visual response effect was improved by utilizing the enhancement effect of polarized violet light coupled with violet light at a close range and the inductive effect of polarized violet light coupled with orange light at a long distance,which provide theoretical support for understanding locust polarotactic orientation mechanisms,facilitate the development of polarization induced light sources for attracting locusts.
文摘数字航空影像和机载点云之间的配准参数精度会直接影响到配准效果,利用共线方程及影像特征点和点云特征点之间计算相似性测度的方法进行参数优化,有效避免了由于初始参数误差导致的配准偏差。首先,提取航空影像及激光雷达(light detection and ranging,LiDAR)点云的特征点;然后,根据初始配准参数及距离误差计算影像与点云之间的匹配点对;最后,通过强制搜索(brute-force,BF)优化方法来寻找更加精确的匹配参数。此外,还构建了2D-3D对应区域的数据集,用于航空影像和机载LiDAR数据配准的相关研究。
文摘Background: The distribution of forest vegetation within urban environments is critically important as it influences urban environmental conditions and the energy exchange through the absorption of solar radiation and modulation of evapotranspiration. It also plays an important role filtering urban water systems and reducing storm water runoff.Methods: We investigate the capacity of ALS data to individually detect, map and characterize large(taller than15 m) trees within the City of Vancouver. Large trees are critical for the function and character of Vancouver’s urban forest. We used an object-based approach for individual tree detection and segmentation to determine tree locations(position of the stem), to delineate the shape of the crowns and to categorize the latter either as coniferous or deciduous.Results: Results indicate a detection rate of 76.6% for trees > 15 m with a positioning error of 2.11 m(stem location). Extracted tree heights possessed a RMSE of 2.60 m and a bias of-1.87 m, whereas crown diameter was derived with a RMSE of 3.85 m and a bias of-2.06 m. Missed trees are principally a result of undetected treetops occurring in dense, overlapping canopies with more accurate detection and delineation of trees in open areas.Conclusion: By identifying key structural trees across Vancouver’s urban forests, we can better understand their role in providing ecosystem goods and services for city residents.
文摘In recent years,propelled by the rapid iterative advancements in digital imaging technology and the semiconductor industry,encompassing microelectronic design,manufacturing,packaging,and testing,time-of-flight(ToF)-based imaging systems for acquiring depth information have garnered considerable attention from both academia and industry.This technology has emerged as a focal point of research within the realm of 3D imaging.Owing to its relatively straightforward principles and exceptional performance,ToF technology finds extensive applications across various domains including human−computer interaction,autonomous driving,industrial inspection,medical and healthcare,augmented reality,smart homes,and 3D reconstruction,among others.Notably,the increasing maturity of ToF-based LiDAR systems is evident in current developments.This paper comprehensively reviews the fundamental principles of ToF technology and LiDAR systems,alongside recent research advancements.It elucidates the innovative aspects and technical challenges encountered in both transmitter(TX)and receiver(RX),providing detailed discussions on corresponding solutions.Furthermore,the paper explores prospective avenues for future research,offering valuable insights for subsequent investigations.
