Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki...Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.展开更多
： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorith...： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.展开更多
A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects bas...A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm.展开更多
In the current 4th generation(4G)communication network,the base station with the same frequency transmission makes a serious interference among adjacent cells,and information transmission is susceptible to interferenc...In the current 4th generation(4G)communication network,the base station with the same frequency transmission makes a serious interference among adjacent cells,and information transmission is susceptible to interference such as channel multipath fading and occlusion effect.Detecting effectively spectrum signal under low signal-to-noise ratio(SNR),directly affects the whole performance of the wireless communication network system.This paper designs an energy signal detection algorithm based on stochastic resonance technology which transforms noise's signal energy into useful signal energy,and improves output SNR.The energy signal detection algorithm realizes the function of providing effective detection of signal under low SNR,and promotes the performance of the whole communication system.展开更多
In traffic-monitoring systems,numerous vision-based approaches have been used to detect vehicle parameters.However,few of these approaches have been used in waterway transport because of the complexity created by fact...In traffic-monitoring systems,numerous vision-based approaches have been used to detect vehicle parameters.However,few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object.In this paper,we present an approach to detecting the parameters of a moving ship in an inland river.This approach involves interactive calibration without a calibration reference.We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios,and we detect ship length,width,speed,and flow.We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90%for all parameters.展开更多
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose...Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.展开更多
Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying thes...Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying these craters is essential for planetary science and is currently mainly achieved with deep learning-driven detection algorithms.However,because impact crater characteristics are substantially affected by the geologic environment,surface materials,and atmospheric conditions,the performance of deep learning models can be inconsistent between celestial bodies.In this paper,we first examine how the surface characteristics of the Moon,Mars,and Earth,along with the differences in their impact crater features,affect model performance.Then,we compare crater detection across celestial bodies by analyzing enhanced convolutional neural networks and U-shaped Convolutional Neural Network-based models to highlight how geology,data,and model design affect accuracy and generalization.Finally,we address current deep learning challenges,suggest directions for model improvement,such as multimodal data fusion and cross-planet learning and list available impact crater databases.This review can provide necessary technical support for deep space exploration and planetary science,as well as new ideas and directions for future research on automatic detection of impact craters on celestial body surfaces and on planetary geology.展开更多
High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration inf...High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration information is susceptible to noise, which leads to its useful signal being drowned by noise. A fusion algorithm of variational modal decomposition(VMD) and improved wavelet threshold filtering is proposed, which is used in combination with tunable diode laser absorption spectroscopy(TDLAS) to implement a non-contact, high-resolution methane gas concentration detection. The fusion algorithm can perform noise reduction and further segmentation of the methane gas detection signal. And the simulation and experiment verify the effectiveness of the fusion algorithm, and the experimental results show that for the detection of air containing 10 ppm, 30 ppm, 60 ppm, 80 ppm, and 99 ppm methane, the errors are 12.75%, 8.18%, 3.37%, 2.46%, and 1.78%, respectively.展开更多
In order to alleviate the impact of radio frequency interference(RFI)on the accuracy of ocean salinity satellite remote sensing,scholars have proposed various detection and labeling algorithms for RFI based on remote ...In order to alleviate the impact of radio frequency interference(RFI)on the accuracy of ocean salinity satellite remote sensing,scholars have proposed various detection and labeling algorithms for RFI based on remote sensing data from the SMOS satellite.However,the signals that generate RFI are diverse,and the factors that influence remote sensing observation data are complex.Existing algorithms often target specific hypothetical conditions,lacking general applicability,which frequently leads to an important gap between the nominal performance of the literature and practical applications,posing great challenges to data labeling work.To address this problem,this study conducted a comprehensive and systematic analysis of RFI simulation based on scene modeling,algorithm modeling,and RFI energy modeling.Three typical RFI detection algorithms were selected,and the simulation scene was divided into 3 typical scenes:ocean,land,and sea–land scenes,and RFI was analyzed in terms of weak,moderate,strong,and extremely strong based on energy.Through simulation analysis and evaluation of RFI detection algorithms,lookup tables for algorithm selection,detection rate,and false-positive rate have been established for different intensities of independent RFI sources and multiple nearby RFI sources in the above scenario.These lookup tables have universal guiding significance and provide reliability assurance in complex situations.展开更多
In this paper, the morphological filter based on parametric edge detection is presented and applied to imaging ladar image with speckle noise. This algorithm and Laplacian of Gaussian (LOG) operator are compared on ed...In this paper, the morphological filter based on parametric edge detection is presented and applied to imaging ladar image with speckle noise. This algorithm and Laplacian of Gaussian (LOG) operator are compared on edge detection. The experimental results indicate the superior performance of this kind of the edge detection.展开更多
The model of linear frequency modulation continuous wave （LFMCW） applied in underwater detection and the method for the detection of echo signal and the estimation of target parameters were studied. By analyzing the...The model of linear frequency modulation continuous wave （LFMCW） applied in underwater detection and the method for the detection of echo signal and the estimation of target parameters were studied. By analyzing the heterodyne signal, an algorithm with the structure of heterodyne-Practional Fourier Transform （FRFT） was proposed. To reduce the computation of searching targets in a two-dimensional FRFT result, the heterodyne signal would be processed by FRFT at a specific order, after Radon-Ambiguity Transform （RAT） was applied to estimate the sweep rate of the signal. Simulations proved that the algorithm can eliminate the coupling phenomenon of distance and velocity of LFMCW, and estimate targets＇ parameters accurately. The lake trial results showed that the processing gain of LFMCW processed by the algorithm in this paper was 13 dB better than that of the LFM processed by matched filter. The research results indicated that the algorithm applied in LFMCW underwater detection was feasible and effective, and it could estimate targets＇ parameters accurately and obtain a good detection performance.展开更多
An adaptive narrowband two-phase Chan-Vese （ANBCV） model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborho...An adaptive narrowband two-phase Chan-Vese （ANBCV） model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF （Markov Random Field, MRF） is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode k-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.展开更多
MIMO technology was proposed as early as 1908 to cope with wireless channel fading.In 1995,Bell Labs was the first to discover the great potential of MIMO system in channel capacity,and in 1996,Foshini of Bell Labs fi...MIMO technology was proposed as early as 1908 to cope with wireless channel fading.In 1995,Bell Labs was the first to discover the great potential of MIMO system in channel capacity,and in 1996,Foshini of Bell Labs first proposed a space-time coding scheme,i.e.,the diagonal-Bell Labs hierarchical space-time model,which can obtain very high spectrum utilization,but due to the complexity of its structure,it is difficult to be applied in practice,and is now rarely investigated.1998,P.W.Wolniansky et al.gave a simple and practical space-time coding scheme on this basis,i.e.,the vertical-Bell Labs layered space-time model.In 1998,P.W.Wolniansky et al.gave a simple and practical space-time coding scheme on this basis,i.e.,Vertical Bell Labs Layered Space-Time(V-BLAST,Vertical Bell Labs Layered Space-Time)model,which can obtain very high spectrum utilisation and is easy to implement,and therefore has received wide attention once it was proposed.In this paper,we focus on airtime layered codes as well as the ZF detection algorithm and the MMSE detection algorithm in VBLAST systems and improve them to further enhance the performance of the two detection algorithms through sequential serial interference cancellation.展开更多
Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for deton...Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for detonation. For this, the study uses a set of 22,000 X-ray scanned images. After preprocessing with filtering techniques to improve image quality, deep learning methods, such as Convolutional Neural Networks (CNNs), are applied for classification. The results are also compared with Autoencoder and Random Forest algorithms. The results are validated on a second dataset, highlighting the advantages of the adopted approach. Baggage screening is a very important part of the risk assessment and security screening process at airports. Automating the detection of dangerous objects from passenger baggage X-ray scanners can speed up and increase the efficiency of the entire security procedure.展开更多
On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both ...On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both theoretical interpreting and computer simulation, explains how to measure the wavefront slope difference between two sub apertures through the determination of image displacements on detector plane. It includes a fast and accurate digital algorithm for detecting wavefront disturbance, which is much suitable for realization in such electrical hardwares as digital signal processors.展开更多
6 hours scanning of view field was conducted by Grapes-3DVar detection system.The process is based on cloud detection,combining Grapes-3DVar system with AIRS instrument characteristics to eliminate field of view conta...6 hours scanning of view field was conducted by Grapes-3DVar detection system.The process is based on cloud detection,combining Grapes-3DVar system with AIRS instrument characteristics to eliminate field of view contaminated by cloud,which lays a solid foundation for application of AIRS data in 3-dimensional variational assimilation system.展开更多
Peridynamics(PD)is an effective method for simulating the spontaneous initiation and propagation of tensile cracks in materials.However,it faces great challenges in simulating compression-shear cracking of geomaterial...Peridynamics(PD)is an effective method for simulating the spontaneous initiation and propagation of tensile cracks in materials.However,it faces great challenges in simulating compression-shear cracking of geomaterials due to the lack of efficient contact-friction models.This paper introduces an original contact-friction model that leverages twin mesh and potential function principles within PD to model rock cracking under tensile and compressive stresses.The contact detection algorithm,based on space segmentation axis-aligned bounding box(AABB)tree data structure,is used to address the significant challenge of highly efficient contact detection in compression and shear problems.In this method,the twin mesh and potential function are utilized to quantify contact detection and contact degree,as well as friction behavior.This is in contrast to the distance and circular contact area model,which lacks physical significance in the classical PD method.As demonstrated by the tests on specimens containing cracks,the proposed model can capture 8 types of secondary fractures,reduce the contact detection error by about 29%e56%,and increase the contact retrieval efficiency by over 1600 times compared to the classic PD models.This significantly enhances the capability of PD to simulate the initiation,expansion,and coalescence of intricate compression-shear cracks.展开更多
As the performance of the box-type multiple launch rocket system(BMLRS)improves,its mechanical structures,particularly the plane clearance design between the slider on the rocket and the guide inside the launch canist...As the performance of the box-type multiple launch rocket system(BMLRS)improves,its mechanical structures,particularly the plane clearance design between the slider on the rocket and the guide inside the launch canister,have grown increasingly complex.However,deficiencies still exist in the current launch modeling theory for BMLRS.In this study,a multi-rigid-flexible-body launch dynamics model coupling the launch platform and rocket was established using the multibody system transfer matrix method and the Newton-Euler formulation.Furthermore,considering the bending of the launch canister,a detection algorithm for slider-guide plane clearance contact was proposed.To quantify the contact force and friction effect between the slider and guide,the contact force model and modified Coulomb model were introduced.Both the modal and launch tests were conducted.Additionally,the modal convergence was verified.By comparing the modal experiments and simulation results,the maximum relative error of the eigenfrequency is 3.29%.thereby verifying the accuracy of the developed BMLRS dynamics model.Furthermore,the launch test validated the proposed plane clearance contact model.Moreover,the study investigated the influence of various model parameters on the dynamic characteristics of BMLRS,including launch canister bending stiffness,slider and guide material,slider-guide clearance,slider length and layout.This analysis of influencing factors provides a foundation for future optimization in BMLRS design.展开更多
Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop cra...Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts： crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi （KLT） detector. Matrix-pattern-oriented least squares support vector machine （MatLSSVM）, as the matrixization version of least square support vector machine （SVM）, inherits the advantages of least squares support vector machine （LSSVM）, reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection.展开更多
Up to now,detailedstrategies and algorithms of automaticchange detection for road networksbased on GIS have not been discussed.This paper discusses two differentstrategies of automatic change detec-tion for images wit...Up to now,detailedstrategies and algorithms of automaticchange detection for road networksbased on GIS have not been discussed.This paper discusses two differentstrategies of automatic change detec-tion for images with low resolution andhigh resolution using old GIS data,and presents a buffer detection andtracing algorithm for detecting roadfrom low-resolution images and a newprofile tracing algorithm for detectingroad from high-resolution images.Forfeature-level change detection(FL-CD),a so-called buffer detection algo-rithm is proposed to detect changes offeatures.Some ideas and algorithms ofusing GIS prior information and somecontext information such as substructures of road in high-resolution imagesto assist road detection and extractionare described in detail.展开更多
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
基金the National Natural Science Foundation of China(No.61165008)
文摘： This paper proposes a new sequential similarity detection algorithm （SSDA）, which can overcome matching error caused by grayscale distortion; meanwhile, time consumption is much less than that of regular algorithms based on image feature. The algorithm adopts Sobel operator to deal with subgraph and template image, and regards the region which has maximum relevance as final result. In order to solve time-consuming problem existing in original algorithm, a coarse-to-fine matching method is put forward. Besides, the location correlation keeps updating and remains the minimum value in the whole scanning process, which can significantly decrease time consumption. Experiments show that the algorithm proposed in this article can not only overcome gray distortion, but also ensure accuracy. Time consumption is at least one time orders of magnitude shorter than that of primal algorithm.
基金National Science and Technology Major Project of China(No.2016ZX04003001)。
文摘A ceramic ball is a basic part widely used in precision bearings.There is no perfect testing equipment for ceramic ball surface defects at present.A fast visual detection algorithm for ceramic ball surface defects based on fringe reflection is designed.By means of image preprocessing,grayscale value accumulative differential positioning,edge detection,pixel-value row difference and template matching,the algorithm can locate feature points and judge whether the spherical surface has defects by the number of points.Taking black silicon nitride ceramic balls with a diameter of 6.35 mm as an example,the defect detection time for a single gray scale image is 0.78 s,and the detection limit is 16.5μm.
基金the Natural Science Foundation of Heilongjiang Province(No.F2015019)the Postdoctoral Foundation of Heilongjiang Province(No.LBHZ16054)the Undergraduate Basic Scientific Research Service Fee Project of Heilongjiang Province(No.Hkdqg201806)
文摘In the current 4th generation(4G)communication network,the base station with the same frequency transmission makes a serious interference among adjacent cells,and information transmission is susceptible to interference such as channel multipath fading and occlusion effect.Detecting effectively spectrum signal under low signal-to-noise ratio(SNR),directly affects the whole performance of the wireless communication network system.This paper designs an energy signal detection algorithm based on stochastic resonance technology which transforms noise's signal energy into useful signal energy,and improves output SNR.The energy signal detection algorithm realizes the function of providing effective detection of signal under low SNR,and promotes the performance of the whole communication system.
基金supported by Fund of National Science&Technology monumental projects under Grants NO.61401239,NO.2012-364-641-209
文摘In traffic-monitoring systems,numerous vision-based approaches have been used to detect vehicle parameters.However,few of these approaches have been used in waterway transport because of the complexity created by factors such as rippling water and lack of calibration object.In this paper,we present an approach to detecting the parameters of a moving ship in an inland river.This approach involves interactive calibration without a calibration reference.We detect a moving ship using an optimized visual foreground detection algorithm that eliminates false detection in dynamic water scenarios,and we detect ship length,width,speed,and flow.We trialed our parameter-detection technique in the Beijing-Hangzhou Grand Canal and found that detection accuracy was greater than 90%for all parameters.
文摘Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.
基金funded by the National Natural Science Foundation of China(12363009 and 12103020)Natural Science Foundation of Jiangxi Province(20224BAB211011)+1 种基金Youth Talent Project of Science and Technology Plan of Ganzhou(2022CXRC9191 and 2023CYZ26970)Jiangxi Province Graduate Innovation Special Funds Project(YC2024-S529 and YC2023-S672).
文摘Planetary surfaces,shaped by billions of years of geologic evolution,display numerous impact craters whose distribution of size,density,and spatial arrangement reveals the celestial body's history.Identifying these craters is essential for planetary science and is currently mainly achieved with deep learning-driven detection algorithms.However,because impact crater characteristics are substantially affected by the geologic environment,surface materials,and atmospheric conditions,the performance of deep learning models can be inconsistent between celestial bodies.In this paper,we first examine how the surface characteristics of the Moon,Mars,and Earth,along with the differences in their impact crater features,affect model performance.Then,we compare crater detection across celestial bodies by analyzing enhanced convolutional neural networks and U-shaped Convolutional Neural Network-based models to highlight how geology,data,and model design affect accuracy and generalization.Finally,we address current deep learning challenges,suggest directions for model improvement,such as multimodal data fusion and cross-planet learning and list available impact crater databases.This review can provide necessary technical support for deep space exploration and planetary science,as well as new ideas and directions for future research on automatic detection of impact craters on celestial body surfaces and on planetary geology.
基金supported by the Project Grant from Heilongjiang Bayi Agricultural Reclamation University,Heilongjiang,China (No.XDB201813)。
文摘High-precision methane gas detection is of great importance in industrial safety, energy production and environmental protection, etc. However, in the existing measurement techniques, the methane gas concentration information is susceptible to noise, which leads to its useful signal being drowned by noise. A fusion algorithm of variational modal decomposition(VMD) and improved wavelet threshold filtering is proposed, which is used in combination with tunable diode laser absorption spectroscopy(TDLAS) to implement a non-contact, high-resolution methane gas concentration detection. The fusion algorithm can perform noise reduction and further segmentation of the methane gas detection signal. And the simulation and experiment verify the effectiveness of the fusion algorithm, and the experimental results show that for the detection of air containing 10 ppm, 30 ppm, 60 ppm, 80 ppm, and 99 ppm methane, the errors are 12.75%, 8.18%, 3.37%, 2.46%, and 1.78%, respectively.
基金supported by the National Natural Science Foundation of China (Grant No. 62031005).
文摘In order to alleviate the impact of radio frequency interference(RFI)on the accuracy of ocean salinity satellite remote sensing,scholars have proposed various detection and labeling algorithms for RFI based on remote sensing data from the SMOS satellite.However,the signals that generate RFI are diverse,and the factors that influence remote sensing observation data are complex.Existing algorithms often target specific hypothetical conditions,lacking general applicability,which frequently leads to an important gap between the nominal performance of the literature and practical applications,posing great challenges to data labeling work.To address this problem,this study conducted a comprehensive and systematic analysis of RFI simulation based on scene modeling,algorithm modeling,and RFI energy modeling.Three typical RFI detection algorithms were selected,and the simulation scene was divided into 3 typical scenes:ocean,land,and sea–land scenes,and RFI was analyzed in terms of weak,moderate,strong,and extremely strong based on energy.Through simulation analysis and evaluation of RFI detection algorithms,lookup tables for algorithm selection,detection rate,and false-positive rate have been established for different intensities of independent RFI sources and multiple nearby RFI sources in the above scenario.These lookup tables have universal guiding significance and provide reliability assurance in complex situations.
文摘In this paper, the morphological filter based on parametric edge detection is presented and applied to imaging ladar image with speckle noise. This algorithm and Laplacian of Gaussian (LOG) operator are compared on edge detection. The experimental results indicate the superior performance of this kind of the edge detection.
文摘The model of linear frequency modulation continuous wave （LFMCW） applied in underwater detection and the method for the detection of echo signal and the estimation of target parameters were studied. By analyzing the heterodyne signal, an algorithm with the structure of heterodyne-Practional Fourier Transform （FRFT） was proposed. To reduce the computation of searching targets in a two-dimensional FRFT result, the heterodyne signal would be processed by FRFT at a specific order, after Radon-Ambiguity Transform （RAT） was applied to estimate the sweep rate of the signal. Simulations proved that the algorithm can eliminate the coupling phenomenon of distance and velocity of LFMCW, and estimate targets＇ parameters accurately. The lake trial results showed that the processing gain of LFMCW processed by the algorithm in this paper was 13 dB better than that of the LFM processed by matched filter. The research results indicated that the algorithm applied in LFMCW underwater detection was feasible and effective, and it could estimate targets＇ parameters accurately and obtain a good detection performance.
基金supported by the National Natural Science Foundation of China(41306086)Technology Innovation Talent Special Foundation of Harbin(2014RFQXJ105)the Fundamental Research Funds for the Central Universities(HEUCF100606)
文摘An adaptive narrowband two-phase Chan-Vese （ANBCV） model is proposed for improving the shadow regions detection performance of sonar images. In the first noise smoothing step, the anisotropic second-order neighborhood MRF （Markov Random Field, MRF） is used to describe the image texture feature parameters. Then, initial two-class segmentation is processed with the block mode k-means clustering algorithm, to estimate the approximate position of the shadow regions. On this basis, the zero level set function is adaptively initialized by the approximate position of shadow regions. ANBCV model is provided to complete local optimization for eliminating the image global interference and obtaining more accurate results. Experimental results show that the new algorithm can efficiently remove partial noise, increase detection speed and accuracy, and with less human intervention.
文摘MIMO technology was proposed as early as 1908 to cope with wireless channel fading.In 1995,Bell Labs was the first to discover the great potential of MIMO system in channel capacity,and in 1996,Foshini of Bell Labs first proposed a space-time coding scheme,i.e.,the diagonal-Bell Labs hierarchical space-time model,which can obtain very high spectrum utilization,but due to the complexity of its structure,it is difficult to be applied in practice,and is now rarely investigated.1998,P.W.Wolniansky et al.gave a simple and practical space-time coding scheme on this basis,i.e.,the vertical-Bell Labs layered space-time model.In 1998,P.W.Wolniansky et al.gave a simple and practical space-time coding scheme on this basis,i.e.,Vertical Bell Labs Layered Space-Time(V-BLAST,Vertical Bell Labs Layered Space-Time)model,which can obtain very high spectrum utilisation and is easy to implement,and therefore has received wide attention once it was proposed.In this paper,we focus on airtime layered codes as well as the ZF detection algorithm and the MMSE detection algorithm in VBLAST systems and improve them to further enhance the performance of the two detection algorithms through sequential serial interference cancellation.
文摘Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for detonation. For this, the study uses a set of 22,000 X-ray scanned images. After preprocessing with filtering techniques to improve image quality, deep learning methods, such as Convolutional Neural Networks (CNNs), are applied for classification. The results are also compared with Autoencoder and Random Forest algorithms. The results are validated on a second dataset, highlighting the advantages of the adopted approach. Baggage screening is a very important part of the risk assessment and security screening process at airports. Automating the detection of dangerous objects from passenger baggage X-ray scanners can speed up and increase the efficiency of the entire security procedure.
文摘On the basis of Hartmann Shack sensor imaging analysis, a new method is presented with which the wavefront slope can be obtained when the object is incoherent and extended. This method, which is demonstrated by both theoretical interpreting and computer simulation, explains how to measure the wavefront slope difference between two sub apertures through the determination of image displacements on detector plane. It includes a fast and accurate digital algorithm for detecting wavefront disturbance, which is much suitable for realization in such electrical hardwares as digital signal processors.
文摘6 hours scanning of view field was conducted by Grapes-3DVar detection system.The process is based on cloud detection,combining Grapes-3DVar system with AIRS instrument characteristics to eliminate field of view contaminated by cloud,which lays a solid foundation for application of AIRS data in 3-dimensional variational assimilation system.
基金supported by the National Natural Science Foundation of China(Grant No.52278333)the China Scholarship Council(CSC)and the Science and Technology Department of Liaoning Province(Grant No.2024JH2/102500069).
文摘Peridynamics(PD)is an effective method for simulating the spontaneous initiation and propagation of tensile cracks in materials.However,it faces great challenges in simulating compression-shear cracking of geomaterials due to the lack of efficient contact-friction models.This paper introduces an original contact-friction model that leverages twin mesh and potential function principles within PD to model rock cracking under tensile and compressive stresses.The contact detection algorithm,based on space segmentation axis-aligned bounding box(AABB)tree data structure,is used to address the significant challenge of highly efficient contact detection in compression and shear problems.In this method,the twin mesh and potential function are utilized to quantify contact detection and contact degree,as well as friction behavior.This is in contrast to the distance and circular contact area model,which lacks physical significance in the classical PD method.As demonstrated by the tests on specimens containing cracks,the proposed model can capture 8 types of secondary fractures,reduce the contact detection error by about 29%e56%,and increase the contact retrieval efficiency by over 1600 times compared to the classic PD models.This significantly enhances the capability of PD to simulate the initiation,expansion,and coalescence of intricate compression-shear cracks.
基金supported by National Natural Science Foundation of China(Grant No.92266201).
文摘As the performance of the box-type multiple launch rocket system(BMLRS)improves,its mechanical structures,particularly the plane clearance design between the slider on the rocket and the guide inside the launch canister,have grown increasingly complex.However,deficiencies still exist in the current launch modeling theory for BMLRS.In this study,a multi-rigid-flexible-body launch dynamics model coupling the launch platform and rocket was established using the multibody system transfer matrix method and the Newton-Euler formulation.Furthermore,considering the bending of the launch canister,a detection algorithm for slider-guide plane clearance contact was proposed.To quantify the contact force and friction effect between the slider and guide,the contact force model and modified Coulomb model were introduced.Both the modal and launch tests were conducted.Additionally,the modal convergence was verified.By comparing the modal experiments and simulation results,the maximum relative error of the eigenfrequency is 3.29%.thereby verifying the accuracy of the developed BMLRS dynamics model.Furthermore,the launch test validated the proposed plane clearance contact model.Moreover,the study investigated the influence of various model parameters on the dynamic characteristics of BMLRS,including launch canister bending stiffness,slider and guide material,slider-guide clearance,slider length and layout.This analysis of influencing factors provides a foundation for future optimization in BMLRS design.
基金co-supported by the National Natural Science Foundation of China (No. 61203170)the Fundamental Research Funds for the Central Universities (No. NS2012026)Startup Foundation for Introduced Talents of Nanjing University of Aeronautics and Astronautics (No. 1007-YAH10047)
文摘Impacted craters are commonly found on the surface of planets, satellites, asteroids and other solar system bodies. In order to speed up the rate of constructing the database of craters, it is important to develop crater detection algorithms. This paper presents a novel approach to automatically detect craters on planetary surfaces. The approach contains two parts： crater candidate region selection and crater detection. In the first part, crater candidate region selection is achieved by Kanade-Lucas-Tomasi （KLT） detector. Matrix-pattern-oriented least squares support vector machine （MatLSSVM）, as the matrixization version of least square support vector machine （SVM）, inherits the advantages of least squares support vector machine （LSSVM）, reduces storage space greatly and reserves spatial redundancies within each image matrix compared with general LSSVM. The second part of the approach employs MatLSSVM to design classifier for crater detection. Experimental results on the dataset which comprises 160 preprocessed image patches from Google Mars demonstrate that the accuracy rate of crater detection can be up to 88%. In addition, the outstanding feature of the approach introduced in this paper is that it takes resized crater candidate region as input pattern directly to finish crater detection. The results of the last experiment demonstrate that MatLSSVM-based classifier can detect crater regions effectively on the basis of KLT-based crater candidate region selection.
基金the Open Research Fund Program of LIESMARS of Wuhan University(No.(01)0304).
文摘Up to now,detailedstrategies and algorithms of automaticchange detection for road networksbased on GIS have not been discussed.This paper discusses two differentstrategies of automatic change detec-tion for images with low resolution andhigh resolution using old GIS data,and presents a buffer detection andtracing algorithm for detecting roadfrom low-resolution images and a newprofile tracing algorithm for detectingroad from high-resolution images.Forfeature-level change detection(FL-CD),a so-called buffer detection algo-rithm is proposed to detect changes offeatures.Some ideas and algorithms ofusing GIS prior information and somecontext information such as substructures of road in high-resolution imagesto assist road detection and extractionare described in detail.
