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
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 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 f...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
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
： 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
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
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
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
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 wi...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
With the help of surgical navigation system,doctors can operate on patients more intuitively and accurately.The positioning accuracy and real-time performance of surgical instruments are very important to the whole sy...With the help of surgical navigation system,doctors can operate on patients more intuitively and accurately.The positioning accuracy and real-time performance of surgical instruments are very important to the whole system.In this paper,we analyze and design the detection algorithm of surgical instrument location mark,and estimate the posture of surgical instrument.In addition,we optimized the pose by remapping.Finally,the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments.The final result shows a high accuracy.展开更多
Considering high-order digital modulation schemes, the bottleneck in consumer products is the detector rather than the modulator. The complexity of the optimal a posteriori probability （APP） detector increases expon...Considering high-order digital modulation schemes, the bottleneck in consumer products is the detector rather than the modulator. The complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of modulated bits per data symbol. Thus, it is necessary to develop low-complexity detection algorithms with an APP-like performance, especially when performing iterative detection, for example in conjunction with bit interleaved coded modulation. We show that a special case of superposition modulation, dubbed Direct Superposition Modulation （DSM）, is particularly suitable for complexity reduction at the receiver side. As opposed to square QAM, DSM achieves capacity without active signal shaping. The main contribution is a low-cost detection algorithm for DSM, which enables iterative detection by taking a priori information into account. This algorithm exploits the approximate piecewise linear behavior of the soft outputs of an APP detector over the entire range of detector input values. A theoretical analysis and simulation results demonstrate that at least max-log APP performance can be reached, while the complexity is significantly reduced compared to classical APP detection.展开更多
Machine learning is becoming increasingly important in scientific and technological progress,due to its ability to create models that describe complex data and generalize well.The wealth of publicly-available seismic ...Machine learning is becoming increasingly important in scientific and technological progress,due to its ability to create models that describe complex data and generalize well.The wealth of publicly-available seismic data nowadays requires automated,fast,and reliable tools to carry out a multitude of tasks,such as the detection of small,local earthquakes in areas characterized by sparsity of receivers.A similar application of machine learning,however,should be built on a large amount of labeled seismograms,which is neither immediate to obtain nor to compile.In this study we present a large dataset of seismograms recorded along the vertical,north,and east components of 1487 broad-band or very broad-band receivers distributed worldwide;this includes 629,0953-component seismograms generated by 304,878 local earthquakes and labeled as EQ,and 615,847 ones labeled as noise(AN).Application of machine learning to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings,even if applied in regions not represented in the training set.Achieving an accuracy of 96.7,95.3,and 93.2% on training,validation,and test set,respectively,we prove that the large variety of geological and tectonic settings covered by our data supports the generalization capabilities of the algorithm,and makes it applicable to real-time detection of local events.We make the database publicly available,intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in signal processing.展开更多
The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource supervision.Almost all the criminal activities take place using weapons mostly a handheld gun,revolv...The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource supervision.Almost all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords etc.Therefore,automatic weapons detection is a vital requirement now a day.The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net.Real time datasets,from local surveillance department’s test sessions are used for model training and testing.Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension.The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution.Efficient-Net algorithm gives better results than existing algorithms.By using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.展开更多
In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system...In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.展开更多
In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detec...In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.展开更多
Error coding is suited when the transmission channel is noisy. This is the case of wireless communication. So to provide a reliable digital data transmission, we should use error detection and correction algorithms. I...Error coding is suited when the transmission channel is noisy. This is the case of wireless communication. So to provide a reliable digital data transmission, we should use error detection and correction algorithms. In this paper, we constructed a simulation study for four detection algorithms. The first three methods—hamming, LRC, and parity are common techniques in networking while the fourth is a proposed one called Signature. The results show that, the hamming code is the best one in term of detection but the worst one in term of execution time. Parity, LRC and signature have the same ability in detecting error, while the signature has a preference than all others methods in term of execution time.展开更多
A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability （APP） detector increases exponentially w...A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of bits per data symbol. This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector. Thus it is important to design new detection algorithms which combine a sufficient performance with low complexity. In this contribution, a detection algorithm for two- dimensional digital modulation schemes which cannot be split-up into real and imaginary parts （like phase shift keying and phase-shifted snperposition modulation （PSM）） is proposed with emphasis on PSM with equal power allocation. This algorithm exploits the relationship between Max-Log-APP detection and a Voronoi diagram to determine planar surfaces of the soft outputs over the entire range of detector input values. As opposed to state-of-the-art detectors based on Voronoi surfaces, a priori information is taken into account, enabling iterative processing. Since the algorithm achieves Max-Log-APP performance, even in the presence of a priori information, this implies a great potential for complexity reduction compared to the classical APP detection.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.
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.
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.
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.
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.
基金supported by the Sichuan Science and Technology Program(2021YFQ0003).
文摘With the help of surgical navigation system,doctors can operate on patients more intuitively and accurately.The positioning accuracy and real-time performance of surgical instruments are very important to the whole system.In this paper,we analyze and design the detection algorithm of surgical instrument location mark,and estimate the posture of surgical instrument.In addition,we optimized the pose by remapping.Finally,the algorithm of location mark detection proposed in this paper and the posture analysis data of surgical instruments are verified and analyzed through experiments.The final result shows a high accuracy.
文摘Considering high-order digital modulation schemes, the bottleneck in consumer products is the detector rather than the modulator. The complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of modulated bits per data symbol. Thus, it is necessary to develop low-complexity detection algorithms with an APP-like performance, especially when performing iterative detection, for example in conjunction with bit interleaved coded modulation. We show that a special case of superposition modulation, dubbed Direct Superposition Modulation （DSM）, is particularly suitable for complexity reduction at the receiver side. As opposed to square QAM, DSM achieves capacity without active signal shaping. The main contribution is a low-cost detection algorithm for DSM, which enables iterative detection by taking a priori information into account. This algorithm exploits the approximate piecewise linear behavior of the soft outputs of an APP detector over the entire range of detector input values. A theoretical analysis and simulation results demonstrate that at least max-log APP performance can be reached, while the complexity is significantly reduced compared to classical APP detection.
文摘Machine learning is becoming increasingly important in scientific and technological progress,due to its ability to create models that describe complex data and generalize well.The wealth of publicly-available seismic data nowadays requires automated,fast,and reliable tools to carry out a multitude of tasks,such as the detection of small,local earthquakes in areas characterized by sparsity of receivers.A similar application of machine learning,however,should be built on a large amount of labeled seismograms,which is neither immediate to obtain nor to compile.In this study we present a large dataset of seismograms recorded along the vertical,north,and east components of 1487 broad-band or very broad-band receivers distributed worldwide;this includes 629,0953-component seismograms generated by 304,878 local earthquakes and labeled as EQ,and 615,847 ones labeled as noise(AN).Application of machine learning to this dataset shows that a simple Convolutional Neural Network of 67,939 parameters allows discriminating between earthquakes and noise single-station recordings,even if applied in regions not represented in the training set.Achieving an accuracy of 96.7,95.3,and 93.2% on training,validation,and test set,respectively,we prove that the large variety of geological and tectonic settings covered by our data supports the generalization capabilities of the algorithm,and makes it applicable to real-time detection of local events.We make the database publicly available,intending to provide the seismological and broader scientific community with a benchmark for time-series to be used as a testing ground in signal processing.
文摘The conventional Close circuit television(CCTV)cameras-based surveillance and control systems require human resource supervision.Almost all the criminal activities take place using weapons mostly a handheld gun,revolver,pistol,swords etc.Therefore,automatic weapons detection is a vital requirement now a day.The current research is concerned about the real-time detection of weapons for the surveillance cameras with an implementation of weapon detection using Efficient–Net.Real time datasets,from local surveillance department’s test sessions are used for model training and testing.Datasets consist of local environment images and videos from different type and resolution cameras that minimize the idealism.This research also contributes in the making of Efficient-Net that is experimented and results in a positive dimension.The results are also been represented in graphs and in calculations for the representation of results during training and results after training are also shown to represent our research contribution.Efficient-Net algorithm gives better results than existing algorithms.By using Efficient-Net algorithms the accuracy achieved 98.12%when epochs increase as compared to other algorithms.
文摘In order to optimize the wood internal quality detection and evaluation system and improve the comprehensive utilization rate of wood,this paper invented a set of log internal defect detection and visualization system by using the ultrasonic dry coupling agent method.The detection and visualization analysis of internal log defects were realized through log specimen test.The main conclusions show that the accuracy,reliability and practicability of the system for detecting the internal defects of log specimens have been effectively verified.The system can make the edge of the detected image smooth by interpolation algorithm,and the edge detection algorithm can be used to detect and reflect the location of internal defects of logs accurately.The content mentioned above has good application value for meeting the requirement of increasing demand for wood resources and improving the automation level of wood nondestructive testing instruments.
文摘In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.
文摘Error coding is suited when the transmission channel is noisy. This is the case of wireless communication. So to provide a reliable digital data transmission, we should use error detection and correction algorithms. In this paper, we constructed a simulation study for four detection algorithms. The first three methods—hamming, LRC, and parity are common techniques in networking while the fourth is a proposed one called Signature. The results show that, the hamming code is the best one in term of detection but the worst one in term of execution time. Parity, LRC and signature have the same ability in detecting error, while the signature has a preference than all others methods in term of execution time.
文摘A challenging task when applying high-order digital modulation schemes is the complexity of the detector. Particularly, the complexity of the optimal a posteriori probability （APP） detector increases exponentially with respect to the number of bits per data symbol. This statement is also true for the Max-Log-APP detector, which is a common simplification of the APP detector. Thus it is important to design new detection algorithms which combine a sufficient performance with low complexity. In this contribution, a detection algorithm for two- dimensional digital modulation schemes which cannot be split-up into real and imaginary parts （like phase shift keying and phase-shifted snperposition modulation （PSM）） is proposed with emphasis on PSM with equal power allocation. This algorithm exploits the relationship between Max-Log-APP detection and a Voronoi diagram to determine planar surfaces of the soft outputs over the entire range of detector input values. As opposed to state-of-the-art detectors based on Voronoi surfaces, a priori information is taken into account, enabling iterative processing. Since the algorithm achieves Max-Log-APP performance, even in the presence of a priori information, this implies a great potential for complexity reduction compared to the classical APP detection.
