In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify ...In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify dangerous targets.Both the rail line and the lane are presented as thin line shapes in the image,but the rail scene is more complex,and the color of the rail line is more difficult to distinguish from the background.By comparison,there are already many deep learning-based lane detection algorithms,but there is a lack of public datasets and targeted deep learning detection algorithms for rail line detection.To address this,this paper constructs a rail image dataset RailwayLine and labels the rail line for the training and testing of models.This dataset contains rich rail images including single-rail,multi-rail,straight rail,curved rail,crossing rails,occlusion,blur,and different lighting conditions.To address the problem of the lack of deep learning-based rail line detection algorithms,we improve the CLRNet algorithm which has an excellent performance in lane detection,and propose the CLRNet-R algorithm for rail line detection.To address the problem of the rail line being thin and occupying fewer pixels in the image,making it difficult to distinguish from complex backgrounds,we introduce an attention mechanism to enhance global feature extraction ability and add a semantic segmentation head to enhance the features of the rail region by the binary probability of rail lines.To address the poor curve recognition performance and unsmooth output lines in the original CLRNet algorithm,we improve the weight allocation for line intersection-over-union calculation in the original framework and propose two loss functions based on local slopes to optimize the model’s local sampling point training constraints,improving the model’s fitting performance on curved rails and obtaining smooth and stable rail line detection results.Through experiments,this paper demonstrates that compared with other mainstream lane detection algorithms,the algorithm proposed in this paper has a better performance for rail line detection.展开更多
The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line dete...The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field.However,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines.The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines.To address the aforementioned challenges,we propose a novel deep learning approach for lane line detection.This method leverages the Swin Transformer in conjunction with LaneNet(called ST-LaneNet).The experience results showed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such as scenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane lines.In 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 Hz.The programming scripts and associated models for this project can be accessed openly at the following GitHub repository:https://github.com/Duane 711/Lane-line-detec tion-ST-LaneNet.展开更多
A new efftcient straight line detection algorithm, GPI （ Gray Projecting Integral） method is proposed. The gray values of a sub-window are projected onto a line, and sum the gray values which are projected onto one ...A new efftcient straight line detection algorithm, GPI （ Gray Projecting Integral） method is proposed. The gray values of a sub-window are projected onto a line, and sum the gray values which are projected onto one same point to shape a special vector, then rotate the projecting direction, obtain many such vectors corresponding to different projecting directions. The vectors can form a matrix, a GPI matrix of the sub-image. The problem of lines detection is converted into maxima or minima searching problem in the GPI matrix. Bused on the GPI matrix, the lines can be calculated. Different from traditional methods, the algorithm can detect the positions of lines accurately, quickly without previous edge detection, which costs less time, and avoids the error resulted from the poor threshold with traditional methods. This algorithm is useful and efftcient for numerous image understanding applications and robot visual navigation, especially for welded joint position detection in heavy noise.展开更多
Straight line detection is a fundamental problem in target recognition from remote sensing images since many man-made objects have straight boundaries.In this study,an integrated straight line detection method for rem...Straight line detection is a fundamental problem in target recognition from remote sensing images since many man-made objects have straight boundaries.In this study,an integrated straight line detection method for remote sensing images is proposed.In this method,the edge-based straight lines are extracted using a chain code tracing method and the phasebased straight lines are extracted using a phase grouping method.The two types of lines are combined using a rule-based feature fusion method by removing redundant line extraction.Since this method integrates the specialties of edge-and phase-based straight line detection methods,it can detect straight lines from remote sensing images with high correctness and robustness.展开更多
Accurate perception of lane line information is one of the basic requirements of unmanned driving technology, which is related to the localization of the vehicle and the determination of the forward direction. In this...Accurate perception of lane line information is one of the basic requirements of unmanned driving technology, which is related to the localization of the vehicle and the determination of the forward direction. In this paper, multi-level constraints are added to the lane line detection model PINet, which is used to improve the perception of lane lines. Predicted lane lines in the network are predicted to have real and imaginary attributes, which are used to enhance the perception of features around the lane lines, with pixel-level constraints on the lane lines;images are converted to bird’s-eye views, where the parallelism between lane lines is reconstructed, with lane line-level constraints on the predicted lane lines;and vanishing points are used to focus on the image hierarchy, with image-level constraints on the lane lines. The model proposed in this paper meets both accuracy (96.44%) and real-time (30 + FPS) requirements, has been tested on the highway on the ground, and has performed stably.展开更多
The valuation relation of potential difference with discharging time in Electrical Discharge Machining (EDM) is analyzed theoretically and tested and verified by experiments designed in this paper and the relation bet...The valuation relation of potential difference with discharging time in Electrical Discharge Machining (EDM) is analyzed theoretically and tested and verified by experiments designed in this paper and the relation between potential difference and spark location is induced and analyzed, and proceed by experiments under the condition of onedimension.展开更多
Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning...Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning is whether it can accurately and quickly detect lane lines.Since 1990 s,they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road.After then,the accuracy for particular situations,the robustness for a wide range of scenarios,time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject.At present,these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories:the traditional image processing and semantic segmentation(includes deep learning)methods.The former mainly involves feature-based and model-based steps,and which can be classified into similarity-and discontinuity-based ones;and the model-based step includes different parametric straight line,curve or pattern models.The semantic segmentation includes different machine learning,neural network and deep learning methods,which is the new trend for the research and application of lane line departure warning systems.This paper describes and analyzes the lane line departure warning systems,image processing algorithms and semantic segmentation methods for lane line detection.展开更多
Effective features are essential for fault diagnosis.Due to the faint characteristics of a single line-to-ground(SLG)fault,fault line detection has become a challenge in resonant grounding distribution systems.This pa...Effective features are essential for fault diagnosis.Due to the faint characteristics of a single line-to-ground(SLG)fault,fault line detection has become a challenge in resonant grounding distribution systems.This paper proposes a novel fault line detection method using waveform fusion and one-dimensional convolutional neural networks(1-D CNN).After an SLG fault occurs,the first-half waves of zero-sequence currents are collected and superimposed with each other to achieve waveform fusion.The compelling feature of fused waveforms is extracted by 1-D CNN to determine whether the fused waveform source contains the fault line.Then,the 1-D CNN output is used to update the value of the counter in order to identify the fault line.Given the lack of fault data in existing distribution systems,the proposed method only needs a small quantity of data for model training and fault line detection.In addition,the proposed method owns fault-tolerant performance.Even if a few samples are misjudged,the fault line can still be detected correctly based on the full output results of 1-D CNN.Experimental results verified that the proposed method can work effectively under various fault conditions.展开更多
In industrial,the modern intelligent degree of board counting is relatively low.Some small manufacturers still count by hand,but for large factories,manual counting needs a lot of human resources,and the accuracy of c...In industrial,the modern intelligent degree of board counting is relatively low.Some small manufacturers still count by hand,but for large factories,manual counting needs a lot of human resources,and the accuracy of counting is low.With the rapid development of modern intelligence,image recognition is becoming more mature,some traditional algorithms keep emerging,such as Hough Transform,Fast Line Detector(FLD),Line Segment Detector(LSD)and other line detection algorithms,they have their own advantages and disadvantages,and are summarized and tested,compare which algorithm has higher accuracy and better effect in the field of board linear detection and counting.Finally,the operation mechanism,advantages and disadvantages are summarized,and the process and trend of the further optimization and development of the traditional algorithm line detection technology in the future are prospected,which provides some reference for the research in related fields.展开更多
A line laser with high power as the background light source for the design of a new photoelectric detection target is proposed in this paper, aiming to improve the detection ability of the traditional photoelectric de...A line laser with high power as the background light source for the design of a new photoelectric detection target is proposed in this paper, aiming to improve the detection ability of the traditional photoelectric detection target under low background illumination. The laser emitted pulse waveform function and the laser echo pulse response function were used to establish the mathematical model of the reflected echo power of projectile in the detection area and derive the calculation function of minimum detectable echo power in the line laser detection screen, according to information of the line laser emitted power, incident angle of projectile, duration time and detection distance of projectile passing through the line laser detection screen. Calculations and experimental results showed that the design method of line laser detection screen and calculation model of laser echo power are reasonable, and the detection ability of line laser detection screen is obviously higher than that of traditional photoelectric detection screen, especially in low background illumination;at the same time, the designed line laser detection screen was used to combine a six line laser detection screen intersection test system, based on live ammunition for shooting. The test system is stable and able to obtain the dynamic parameters of the flying projectile, verifying that the design of the line laser detection screen in new photoelectric detection target can be suitable for shooting range test applications.展开更多
In order to improve the performance of line spectrum detection,according to the feature that the underwater target radiated noise containing stable line spectrum,the differences of the phase difference between line sp...In order to improve the performance of line spectrum detection,according to the feature that the underwater target radiated noise containing stable line spectrum,the differences of the phase difference between line spectrum and background noise,a weighted line spectrum detection algorithm based on the phase variance is proposed in frequency domain.After phase difference alignment,the phase variance of line spectrum and the phase of background noise,respectively,are small and big in frequency domain,this method utilizes the weighted statistical algorithm to cumulate the frequency spectrum based on the phase variance,which can restrain the background noise disturbance,and enhance the signal to noise ratio（SNR）.The theory analysis and experimental results both verify that the proposed method can well enhance the energy of line spectrum,restrain the energy of background noise,and have better detection performance under lower SNR.展开更多
Corona discharge is a common phenomenon in power transmission lines external insulation, and it may cause serious defect if without effective detection. The ultraviolet (UV) imagery technology has been widely used to ...Corona discharge is a common phenomenon in power transmission lines external insulation, and it may cause serious defect if without effective detection. The ultraviolet (UV) imagery technology has been widely used to detect the corona discharge in industry in recent years, but some influence factors’ functions are not definite. In this paper, the fracture aluminum strands which is common in power transmission lines were used as the electrode model while a SuperB ultraviolet imager were utilized to detect, the photon count rate was detected with different detect distance, electric field, aluminum strands length and UV gain were applied. Then the multivariate regression analysis (MRA) was taken to calculate the function between the photon count and the factors.展开更多
The noise's statistical characteristics are very important for signal detection.In this paper,the ambient noise statistical characteristics are investigated by using the recorded noise data in sea trials first,and...The noise's statistical characteristics are very important for signal detection.In this paper,the ambient noise statistical characteristics are investigated by using the recorded noise data in sea trials first,and the results show that the generalized Gaussian distribution is a suitable model for the ambient noise modeling.Thereafter,the optimal detector based on maximum likelihood ratio can be deduced,and the asymptotic detector is also derived under weak signal assumption.The detector's performance is verified by using numerical simulation,and the results showthat the optimal and asymptotic detectors outperform the conventional correlation-integration system due to accuracy modeling of ambient noise.展开更多
On-line chatter detection can avoid unstable cutting through monitoring the machining process.In order to identify chatter in a timely manner,an improved Support Vector Machine(SVM)is developed in this paper,based on ...On-line chatter detection can avoid unstable cutting through monitoring the machining process.In order to identify chatter in a timely manner,an improved Support Vector Machine(SVM)is developed in this paper,based on extracted features.In the SVM model,the penalty factor(e)and the core parameter(g)have important influence on the classification,more than from Kernel Functions(KFs).Hence,first the classification results are conducted using different KFs.Then two methods are presented for exploring the best parameters.The chatter identification results show that the Genetic Algorithm(GA)approach is more suitable for deciding the parameters than the Grid Explore(GE)approach.展开更多
The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Si...The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.展开更多
With increasing need of high quality movie, more and more standard resolution films are upconverted to the high-resolution films. After this operation, the defects exist in the old movie are more obvious because they ...With increasing need of high quality movie, more and more standard resolution films are upconverted to the high-resolution films. After this operation, the defects exist in the old movie are more obvious because they are enlarged in size, therefore, an efficient artifacts detection method with more precise result and lower computational complexity is in need. This paper provided a line scratch mathematical model, which derives from the Kokaram model and Bruni model, and then gave a detection method to meet the requirements of the high-resolution video application.展开更多
Microfluidic analytical system was developed based on annular flow of phase separation multiphase flow with a ternary water-hydrophilic/hydrophobic organic solvent solution. The analytical system was combined with on-...Microfluidic analytical system was developed based on annular flow of phase separation multiphase flow with a ternary water-hydrophilic/hydrophobic organic solvent solution. The analytical system was combined with on-line luminol chemiluminescence detection for catechin analysis. The water (10 mM phosphate buffer, pH 7.3)-acetonitrile-ethyl acetate mixed solution (3:8:4, volume ratio) containing 60 μM luminol and 2 mM hydrogen peroxide as a carrier was fed into the capillary tube (open-tubular fused-silica, 75 μm inner diameter, 110 cm effective length) at a flow rate of 1.0 μL·min-1. The carrier solution showed stable chemiluminescence as a baseline on the flow chart. Eight catechins were detected as negative peaks for their antioxidant potential with different detection times. The system was applied to analyze the amounts of catechin in commercially available green tea beverages.展开更多
In China, the purity of maize hybrid strain is discomforting to the development of seed industrialization. Finding a new method for reproduction of maize hybrid strain is necessary. In this study, using particle bomba...In China, the purity of maize hybrid strain is discomforting to the development of seed industrialization. Finding a new method for reproduction of maize hybrid strain is necessary. In this study, using particle bombardment, barstar gene was transferred into maize inbred line 18-599 （White）, which is an antiviral and high quality maize inbred line. By molecular detection of the anther of transgenic maize, two plants transferred with barstar gene were gained in this study, which are two restorer lines. The two plants showed normal male spike, and lively microspores. But the capacity of the two restorer lines should be studied in the future. The aim of this study is to find a new method of reproduction of maize hybrid strain using engineering restorer lines and engineering sterility lines by gene engineering technology.展开更多
Detecting internal defects,particularly voids behind linings,is critical for ensuring the structural integrity of aging high-speed rail(HSR)tunnel networks.While ground-penetrating radar(GPR)is widely employed,systema...Detecting internal defects,particularly voids behind linings,is critical for ensuring the structural integrity of aging high-speed rail(HSR)tunnel networks.While ground-penetrating radar(GPR)is widely employed,systematic quantification of performance boundaries for air-coupled(A-CGPR)and ground-coupled(G-CGPR)systems within the complex electromagnetic environment of multilayer reinforced HSR tunnels remains limited.This study establishes physics-based quantitative performance limits for A-CGPR and G-CGPR through rigorously validated GPRMax finite-difference time-domain(FDTD)simulations and comprehensive field validation over a 300 m operational HSR tunnel section.Key performance metrics were quantified as functions of:(a)detection distance(A-CGPR:2.0–4.5 m;G-CGPR:≤0.1 m),(b)antenna frequency(A-CGPR:300 MHz;G-CGPR:400/900 MHz),(c)reinforcement configuration(unreinforced,single-layer,multilayer rebar),and(d)void geometry(axial length:0.1–1.0 m;radial depth:0.1–0.5 m).Key findings demonstrate:a.A-CGPR(300 MHz):Reliably detects axial voids≥0.3 m at distances≤3 m in minimally reinforced(single-layer rebar)linings(field R2=0.89).Performance degrades significantly at distances>3 m(>60%signal attenuation at 4.5 m)or under multilayer rebar interference,causing 25%–40%accuracy loss for voids<0.3 m.Optimal distance:2.0–2.5 m.b.G-CGPR(900 MHz):Achieves<5%size measurement error for axial voids≥0.1 m and radial voids≥0.2 m in unreinforced linings.Resolution degrades under multilayer reinforcement due to severe signal attenuation,increasing axial void detection error to 10%–20%for voids≥0.3 m and constraining radial size measurement.c.Synergistic Framework:A hybrid inspection protocol is proposed,integrating A-CGPR(20 km/h)for rapid large-area screening and targeted G-CGPR(3 km/h)for high-resolution verification of identified anomalies.This framework enhances NDT efficiency while reducing estimated lifecycle inspection costs by 34%compared to G-CGPR alone.This research provides the first physics-derived quantitative detection thresholds for A-CGPR and G-CGPR in multi-rebar HSR tunnels,validated through field-correlated simulations.Future work will focus on multi-frequency antenna arrays and deep learning algorithms to mitigate reinforcement interference.The established performance boundaries and hybrid framework offer critical guidance for optimizing tunnel lining inspection strategies in extensive HSR networks.展开更多
Cartoons are a worldwide popular visual entertainment medium with a long history. Nowadays,with the boom of electronic devices, there is an increasing need to digitize old classic cartoons as a basis for further editi...Cartoons are a worldwide popular visual entertainment medium with a long history. Nowadays,with the boom of electronic devices, there is an increasing need to digitize old classic cartoons as a basis for further editing, including deformation,colorization, etc. To perform such editing, it is essential to extract the structure lines within cartoon images.Traditional edge detection methods are mainly based on gradients. These methods perform poorly in the face of compression artifacts and spatially-varying line colors,which cause gradient values to become unreliable. This paper presents the first approach to extract structure lines in cartoons based on regions. Our method starts by segmenting an image into regions, and then classifies them as edge regions and non-edge regions. Our second main contribution comprises three measures to estimate the likelihood of a region being a non-edge region.These measure darkness, local contrast, and shape.Since the likelihoods become unreliable as regions become smaller, we further classify regions using both likelihoods and the relationships to neighboring regions via a graph-cut formulation. Our method has been evaluated on a wide variety of cartoon images, and convincing results are obtained in all cases.展开更多
基金the Sichuan Science and Technology Program(No.2022YFS0557)the National Natural Science Foundation of China(No.61972271)。
文摘In smart driving for rail transit,a reliable obstacle detection system is an important guarantee for the safety of trains.Therein,the detection of the rail area directly affects the accuracy of the system to identify dangerous targets.Both the rail line and the lane are presented as thin line shapes in the image,but the rail scene is more complex,and the color of the rail line is more difficult to distinguish from the background.By comparison,there are already many deep learning-based lane detection algorithms,but there is a lack of public datasets and targeted deep learning detection algorithms for rail line detection.To address this,this paper constructs a rail image dataset RailwayLine and labels the rail line for the training and testing of models.This dataset contains rich rail images including single-rail,multi-rail,straight rail,curved rail,crossing rails,occlusion,blur,and different lighting conditions.To address the problem of the lack of deep learning-based rail line detection algorithms,we improve the CLRNet algorithm which has an excellent performance in lane detection,and propose the CLRNet-R algorithm for rail line detection.To address the problem of the rail line being thin and occupying fewer pixels in the image,making it difficult to distinguish from complex backgrounds,we introduce an attention mechanism to enhance global feature extraction ability and add a semantic segmentation head to enhance the features of the rail region by the binary probability of rail lines.To address the poor curve recognition performance and unsmooth output lines in the original CLRNet algorithm,we improve the weight allocation for line intersection-over-union calculation in the original framework and propose two loss functions based on local slopes to optimize the model’s local sampling point training constraints,improving the model’s fitting performance on curved rails and obtaining smooth and stable rail line detection results.Through experiments,this paper demonstrates that compared with other mainstream lane detection algorithms,the algorithm proposed in this paper has a better performance for rail line detection.
基金Supported by National Natural Science Foundation of China(Grant Nos.51605003,51575001)Natural Science Foundation of Anhui Higher Education Institutions of China(Grant No.KJ2020A0358)Young and Middle-Aged Top Talents Training Program of Anhui Polytechnic University of China.
文摘The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection.As deep learning and computer vision technologies evolve,a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field.However,owing to the simple appearance of lane lines and the lack of distinctive features,it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines.The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines.To address the aforementioned challenges,we propose a novel deep learning approach for lane line detection.This method leverages the Swin Transformer in conjunction with LaneNet(called ST-LaneNet).The experience results showed that the true positive detection rate can reach 97.53%for easy lanes and 96.83%for difficult lanes(such as scenes with severe occlusion and extreme lighting conditions),which can better accomplish the objective of detecting lane lines.In 1000 detection samples,the average detection accuracy can reach 97.83%,the average inference time per image can reach 17.8 ms,and the average number of frames per second can reach 64.8 Hz.The programming scripts and associated models for this project can be accessed openly at the following GitHub repository:https://github.com/Duane 711/Lane-line-detec tion-ST-LaneNet.
基金This research was funded by Key Laboratory for Advanced Manufacturing by Materials Processing Technology, Ministry of Education, The Research Fund for the Doctoral Program of Higher Education （No. 20020003053）National Natural Science Foundation of China （ No. 50275083 ）.
文摘A new efftcient straight line detection algorithm, GPI （ Gray Projecting Integral） method is proposed. The gray values of a sub-window are projected onto a line, and sum the gray values which are projected onto one same point to shape a special vector, then rotate the projecting direction, obtain many such vectors corresponding to different projecting directions. The vectors can form a matrix, a GPI matrix of the sub-image. The problem of lines detection is converted into maxima or minima searching problem in the GPI matrix. Bused on the GPI matrix, the lines can be calculated. Different from traditional methods, the algorithm can detect the positions of lines accurately, quickly without previous edge detection, which costs less time, and avoids the error resulted from the poor threshold with traditional methods. This algorithm is useful and efftcient for numerous image understanding applications and robot visual navigation, especially for welded joint position detection in heavy noise.
基金This work is supported by the National Natural Science Foundation of China(No.41171321,40871189)the Natural Science Foundation of the Jiangsu Higher Education Institutions of China(No.11KJA420001)+1 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutionsthe Qin Lan Project of Jiangsu,China.
文摘Straight line detection is a fundamental problem in target recognition from remote sensing images since many man-made objects have straight boundaries.In this study,an integrated straight line detection method for remote sensing images is proposed.In this method,the edge-based straight lines are extracted using a chain code tracing method and the phasebased straight lines are extracted using a phase grouping method.The two types of lines are combined using a rule-based feature fusion method by removing redundant line extraction.Since this method integrates the specialties of edge-and phase-based straight line detection methods,it can detect straight lines from remote sensing images with high correctness and robustness.
文摘Accurate perception of lane line information is one of the basic requirements of unmanned driving technology, which is related to the localization of the vehicle and the determination of the forward direction. In this paper, multi-level constraints are added to the lane line detection model PINet, which is used to improve the perception of lane lines. Predicted lane lines in the network are predicted to have real and imaginary attributes, which are used to enhance the perception of features around the lane lines, with pixel-level constraints on the lane lines;images are converted to bird’s-eye views, where the parallelism between lane lines is reconstructed, with lane line-level constraints on the predicted lane lines;and vanishing points are used to focus on the image hierarchy, with image-level constraints on the lane lines. The model proposed in this paper meets both accuracy (96.44%) and real-time (30 + FPS) requirements, has been tested on the highway on the ground, and has performed stably.
文摘The valuation relation of potential difference with discharging time in Electrical Discharge Machining (EDM) is analyzed theoretically and tested and verified by experiments designed in this paper and the relation between potential difference and spark location is induced and analyzed, and proceed by experiments under the condition of onedimension.
基金financially supported by the National Natural Science Foundation of China(grant No.61170147)the Scientific and Technological Project of Shaanxi Province in China(grant No.2019GY-038)。
文摘Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning is whether it can accurately and quickly detect lane lines.Since 1990 s,they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road.After then,the accuracy for particular situations,the robustness for a wide range of scenarios,time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject.At present,these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories:the traditional image processing and semantic segmentation(includes deep learning)methods.The former mainly involves feature-based and model-based steps,and which can be classified into similarity-and discontinuity-based ones;and the model-based step includes different parametric straight line,curve or pattern models.The semantic segmentation includes different machine learning,neural network and deep learning methods,which is the new trend for the research and application of lane line departure warning systems.This paper describes and analyzes the lane line departure warning systems,image processing algorithms and semantic segmentation methods for lane line detection.
基金supported by the National Natural Science Foundation of China through the Project of Research of Flexible and Adaptive Arc-Suppression Method for Single-Phase Grounding Fault in Distribution Networks(No.51677030).
文摘Effective features are essential for fault diagnosis.Due to the faint characteristics of a single line-to-ground(SLG)fault,fault line detection has become a challenge in resonant grounding distribution systems.This paper proposes a novel fault line detection method using waveform fusion and one-dimensional convolutional neural networks(1-D CNN).After an SLG fault occurs,the first-half waves of zero-sequence currents are collected and superimposed with each other to achieve waveform fusion.The compelling feature of fused waveforms is extracted by 1-D CNN to determine whether the fused waveform source contains the fault line.Then,the 1-D CNN output is used to update the value of the counter in order to identify the fault line.Given the lack of fault data in existing distribution systems,the proposed method only needs a small quantity of data for model training and fault line detection.In addition,the proposed method owns fault-tolerant performance.Even if a few samples are misjudged,the fault line can still be detected correctly based on the full output results of 1-D CNN.Experimental results verified that the proposed method can work effectively under various fault conditions.
基金supported by National Natural Science Foundation of China(No.61775139)Zhejiang Provincial Key R&D Plan(No.2020C02020).
文摘In industrial,the modern intelligent degree of board counting is relatively low.Some small manufacturers still count by hand,but for large factories,manual counting needs a lot of human resources,and the accuracy of counting is low.With the rapid development of modern intelligence,image recognition is becoming more mature,some traditional algorithms keep emerging,such as Hough Transform,Fast Line Detector(FLD),Line Segment Detector(LSD)and other line detection algorithms,they have their own advantages and disadvantages,and are summarized and tested,compare which algorithm has higher accuracy and better effect in the field of board linear detection and counting.Finally,the operation mechanism,advantages and disadvantages are summarized,and the process and trend of the further optimization and development of the traditional algorithm line detection technology in the future are prospected,which provides some reference for the research in related fields.
基金This work has been supported by Project of the National Natural Science Foundation of China(No.62073256,61773305)in part by the Key Science and Technology Program of Shaanxi Province(No.2020GY-125)Xi’an Science and Technology Innovation Talent Service Enterprise Project(No.2020KJRC0041).
文摘A line laser with high power as the background light source for the design of a new photoelectric detection target is proposed in this paper, aiming to improve the detection ability of the traditional photoelectric detection target under low background illumination. The laser emitted pulse waveform function and the laser echo pulse response function were used to establish the mathematical model of the reflected echo power of projectile in the detection area and derive the calculation function of minimum detectable echo power in the line laser detection screen, according to information of the line laser emitted power, incident angle of projectile, duration time and detection distance of projectile passing through the line laser detection screen. Calculations and experimental results showed that the design method of line laser detection screen and calculation model of laser echo power are reasonable, and the detection ability of line laser detection screen is obviously higher than that of traditional photoelectric detection screen, especially in low background illumination;at the same time, the designed line laser detection screen was used to combine a six line laser detection screen intersection test system, based on live ammunition for shooting. The test system is stable and able to obtain the dynamic parameters of the flying projectile, verifying that the design of the line laser detection screen in new photoelectric detection target can be suitable for shooting range test applications.
基金supported by the National Natural Science Foundation of China(61372180)the Young Talent Frontier Project of Institute of Acoustics of Chinese Academy of Sciences(Y454341261)
文摘In order to improve the performance of line spectrum detection,according to the feature that the underwater target radiated noise containing stable line spectrum,the differences of the phase difference between line spectrum and background noise,a weighted line spectrum detection algorithm based on the phase variance is proposed in frequency domain.After phase difference alignment,the phase variance of line spectrum and the phase of background noise,respectively,are small and big in frequency domain,this method utilizes the weighted statistical algorithm to cumulate the frequency spectrum based on the phase variance,which can restrain the background noise disturbance,and enhance the signal to noise ratio（SNR）.The theory analysis and experimental results both verify that the proposed method can well enhance the energy of line spectrum,restrain the energy of background noise,and have better detection performance under lower SNR.
文摘Corona discharge is a common phenomenon in power transmission lines external insulation, and it may cause serious defect if without effective detection. The ultraviolet (UV) imagery technology has been widely used to detect the corona discharge in industry in recent years, but some influence factors’ functions are not definite. In this paper, the fracture aluminum strands which is common in power transmission lines were used as the electrode model while a SuperB ultraviolet imager were utilized to detect, the photon count rate was detected with different detect distance, electric field, aluminum strands length and UV gain were applied. Then the multivariate regression analysis (MRA) was taken to calculate the function between the photon count and the factors.
基金Sponsored by the National Nature Science Foundation of China(11074308)China Postdoctoral Science Foundation(201003754)
文摘The noise's statistical characteristics are very important for signal detection.In this paper,the ambient noise statistical characteristics are investigated by using the recorded noise data in sea trials first,and the results show that the generalized Gaussian distribution is a suitable model for the ambient noise modeling.Thereafter,the optimal detector based on maximum likelihood ratio can be deduced,and the asymptotic detector is also derived under weak signal assumption.The detector's performance is verified by using numerical simulation,and the results showthat the optimal and asymptotic detectors outperform the conventional correlation-integration system due to accuracy modeling of ambient noise.
文摘On-line chatter detection can avoid unstable cutting through monitoring the machining process.In order to identify chatter in a timely manner,an improved Support Vector Machine(SVM)is developed in this paper,based on extracted features.In the SVM model,the penalty factor(e)and the core parameter(g)have important influence on the classification,more than from Kernel Functions(KFs).Hence,first the classification results are conducted using different KFs.Then two methods are presented for exploring the best parameters.The chatter identification results show that the Genetic Algorithm(GA)approach is more suitable for deciding the parameters than the Grid Explore(GE)approach.
文摘The integration of optical images and elevation data is of great importance for 3D-assisted mapping applications. Very high resolution (VHR) satellite images provide ideal geo-data for mapping building information. Since buildings are inherently elevated objects, these images need to be co-registered with their elevation data for reliable building detection results. However, accurate co-registration is extremely difficult for off-nadir VHR images acquired over dense urban areas. Therefore, this research proposes a Disparity-Based Elevation Co-Registration (DECR) method for generating a Line-of-Sight Digital Surface Model (LoS-DSM) to efficiently achieve image-elevation data co-registration with pixel-level accuracy. Relative to the traditional photogrammetric approach, the RMSE value of the derived elevations is found to be less than 2 pixels. The applicability of the DECR method is demonstrated through elevation-based building detection (EBD) in a challenging dense urban area. The quality of the detection result is found to be more than 90%. Additionally, the detected objects were geo-referenced successfully to their correct ground locations to allow direct integration with other maps. In comparison to the original LoS-DSM development algorithm, the DECR algorithm is more efficient by reducing the calculation steps, preserving the co-registration accuracy, and minimizing the need for elevation normalization in dense urban areas.
文摘With increasing need of high quality movie, more and more standard resolution films are upconverted to the high-resolution films. After this operation, the defects exist in the old movie are more obvious because they are enlarged in size, therefore, an efficient artifacts detection method with more precise result and lower computational complexity is in need. This paper provided a line scratch mathematical model, which derives from the Kokaram model and Bruni model, and then gave a detection method to meet the requirements of the high-resolution video application.
文摘Microfluidic analytical system was developed based on annular flow of phase separation multiphase flow with a ternary water-hydrophilic/hydrophobic organic solvent solution. The analytical system was combined with on-line luminol chemiluminescence detection for catechin analysis. The water (10 mM phosphate buffer, pH 7.3)-acetonitrile-ethyl acetate mixed solution (3:8:4, volume ratio) containing 60 μM luminol and 2 mM hydrogen peroxide as a carrier was fed into the capillary tube (open-tubular fused-silica, 75 μm inner diameter, 110 cm effective length) at a flow rate of 1.0 μL·min-1. The carrier solution showed stable chemiluminescence as a baseline on the flow chart. Eight catechins were detected as negative peaks for their antioxidant potential with different detection times. The system was applied to analyze the amounts of catechin in commercially available green tea beverages.
文摘In China, the purity of maize hybrid strain is discomforting to the development of seed industrialization. Finding a new method for reproduction of maize hybrid strain is necessary. In this study, using particle bombardment, barstar gene was transferred into maize inbred line 18-599 （White）, which is an antiviral and high quality maize inbred line. By molecular detection of the anther of transgenic maize, two plants transferred with barstar gene were gained in this study, which are two restorer lines. The two plants showed normal male spike, and lively microspores. But the capacity of the two restorer lines should be studied in the future. The aim of this study is to find a new method of reproduction of maize hybrid strain using engineering restorer lines and engineering sterility lines by gene engineering technology.
基金funded by the Key Project of Science&Technology Research ofChina Academy of Railway Sciences,grant number 2023YJ022.
文摘Detecting internal defects,particularly voids behind linings,is critical for ensuring the structural integrity of aging high-speed rail(HSR)tunnel networks.While ground-penetrating radar(GPR)is widely employed,systematic quantification of performance boundaries for air-coupled(A-CGPR)and ground-coupled(G-CGPR)systems within the complex electromagnetic environment of multilayer reinforced HSR tunnels remains limited.This study establishes physics-based quantitative performance limits for A-CGPR and G-CGPR through rigorously validated GPRMax finite-difference time-domain(FDTD)simulations and comprehensive field validation over a 300 m operational HSR tunnel section.Key performance metrics were quantified as functions of:(a)detection distance(A-CGPR:2.0–4.5 m;G-CGPR:≤0.1 m),(b)antenna frequency(A-CGPR:300 MHz;G-CGPR:400/900 MHz),(c)reinforcement configuration(unreinforced,single-layer,multilayer rebar),and(d)void geometry(axial length:0.1–1.0 m;radial depth:0.1–0.5 m).Key findings demonstrate:a.A-CGPR(300 MHz):Reliably detects axial voids≥0.3 m at distances≤3 m in minimally reinforced(single-layer rebar)linings(field R2=0.89).Performance degrades significantly at distances>3 m(>60%signal attenuation at 4.5 m)or under multilayer rebar interference,causing 25%–40%accuracy loss for voids<0.3 m.Optimal distance:2.0–2.5 m.b.G-CGPR(900 MHz):Achieves<5%size measurement error for axial voids≥0.1 m and radial voids≥0.2 m in unreinforced linings.Resolution degrades under multilayer reinforcement due to severe signal attenuation,increasing axial void detection error to 10%–20%for voids≥0.3 m and constraining radial size measurement.c.Synergistic Framework:A hybrid inspection protocol is proposed,integrating A-CGPR(20 km/h)for rapid large-area screening and targeted G-CGPR(3 km/h)for high-resolution verification of identified anomalies.This framework enhances NDT efficiency while reducing estimated lifecycle inspection costs by 34%compared to G-CGPR alone.This research provides the first physics-derived quantitative detection thresholds for A-CGPR and G-CGPR in multi-rebar HSR tunnels,validated through field-correlated simulations.Future work will focus on multi-frequency antenna arrays and deep learning algorithms to mitigate reinforcement interference.The established performance boundaries and hybrid framework offer critical guidance for optimizing tunnel lining inspection strategies in extensive HSR networks.
基金supported by National Natural Science Foundation of China (Nos. 61272293 and 61103120)Shenzhen Basic Research Project (No. JCYJ20120619152326448)+2 种基金Shenzhen Nanshan Innovative Institution Establishment Fund (No. KC2013ZDZJ0007A)the Research Grants Council of the Hong Kong Special Administrative Region under RGC General Research Fund (No. CUHK 417913)Guangzhou Novo Program of Science & Technology (No. 0501-330)
文摘Cartoons are a worldwide popular visual entertainment medium with a long history. Nowadays,with the boom of electronic devices, there is an increasing need to digitize old classic cartoons as a basis for further editing, including deformation,colorization, etc. To perform such editing, it is essential to extract the structure lines within cartoon images.Traditional edge detection methods are mainly based on gradients. These methods perform poorly in the face of compression artifacts and spatially-varying line colors,which cause gradient values to become unreliable. This paper presents the first approach to extract structure lines in cartoons based on regions. Our method starts by segmenting an image into regions, and then classifies them as edge regions and non-edge regions. Our second main contribution comprises three measures to estimate the likelihood of a region being a non-edge region.These measure darkness, local contrast, and shape.Since the likelihoods become unreliable as regions become smaller, we further classify regions using both likelihoods and the relationships to neighboring regions via a graph-cut formulation. Our method has been evaluated on a wide variety of cartoon images, and convincing results are obtained in all cases.
