Application of transient Rayleigh wave in detection of tunnel lining void area

Transient Rayleigh wave detection is a high-precision nondestructive detection method.At present,it has been widely used in shallow exploration,but rarely used in tunnel lining quality detection.Through the tunnel lining physical model experiment,the layout defects of the double-layer reinforcement lining area were detected and the Rayleigh wave velocity profile and dispersion curve were analyzed after data process-ing,which finally verified the feasibility and accuracy of Rayleigh wave method in detecting the tunnel lining void area.The results show that the method is not affected by the reinforcement inside the lining,the shallow detection is less disturbed and the accuracy is higher,and the data will fluctuate slightly with the deepening of the detection depth.At the same time,this method responds quite accurately to the thickness of the concrete,allowing for the assessment of the tunnel lining’s lack of compactness.This method has high efficiency,good reliability,and simple data processing,and is suitable for nondestructive detection of internal defects of tun-nel lining structure.

ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNet

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.

Lane Line Detection Based on Improved PINet

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.

A new straight line detection method in images for robot seam tracking

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.

Line spectrum detection algorithm based on the phase feature of target radiated noise

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.

A line laser detection screen design and projectile echo power calculation in detection screen area

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.

Statistics Modeling of Shallow Sea Ambient Noise and Its Applications in Low-frequency Line Spectrum Detection

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.

A Study on Fast and Robust Vanishing Point Detection System Using Fast M-Estimation Method and Regional Division for In-vehicle Camera

The vanishing point detection technology helps automatic driving. In this paper, the straight lines on the road associated with the vanishing point are extracted efficiently by using the regional division and angle limitation. And, the vanishing point is detected robustly by using the fast M-estimation method. Proposed method could detect straight-line features associated with vanishing point detection efficient on the road. And the vanishing point was detected exactly by the effect of the fast M-estimation method when the straight-line features not associated with vanishing point detection were detected. The processing time of the proposed method was faster than the camera flame rate （30 fps）. Thus, the proposed method is capable of real-time processing.

Data-driven Detection and Identification of Line Parameters with PMU and Unsynchronized SCADA Measurements in Distribution Grids

Line parameters play an important role in the control and management of distribution systems.Currently,phasor measurement unit(PMU)systems and supervisory control and data acquisition(SCADA)systems coexist in distribution systems.Unfortunately,SCADA and PMU measurements usually do not match each other,resulting in inaccurate detection and identification of line parameters based on measurements.To solve this problem,a data-driven method is proposed.SCADA measurements are taken as samples and PMU measurements as the population.A probability parameter identification index(PPII)is derived to detect the whole line parameter based on the probability density function(PDF)parameters of the measurements.For parameter identification,a power-loss PDF with the PMU time stamps and a power-loss chronological PDF are derived via kernel density estimation(KDE)and a conditional PDF.Then,the power-loss samples with the PMU time stamps and chronological correlations are generated by the two PDFs of the power loss via the Metropolis-Hastings(MH)algorithm.Finally,using the power-loss samples and PMU current measurements,the line parameters are identified using the total least squares(TLS)algorithm.Hardware simulations demonstrate the effectiveness of the proposed method for distribution network line parameter detection and identification.

Research and application of an intelligent recloser controller installed on outdoor rod

A new type of intelligent recolser controller installed on the outdoor rod is developed, which is mainly composed of microcontroller of Intel 87C196KC 20 and CPLD devices. This controller integrates all the functions of measuring, controlling, protection, fault diagnosis, communication, remote controlled operation and self power devices with infra red remote control devices as a unit. The controller applies the distributed structure, field concentration line and intelligent technology to seal up the synthetic servomechanisms such as the microcomputer based protection and measuring devices in the second stage of the mini out door transformer substation, which are distributed on the outdoor circuit switches on the spot and formed as a whole. Therefore, this technology can transform a large number of ordinary homemade SF 6 circuit beaker and vacuum circuit breaker into intelligent circuit recloser, thus replacing the expensive imported automatic circuit recolser.

Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation:A review

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.

Fault Line Detection Using Waveform Fusion and One-dimensional Convolutional Neural Network in Resonant Grounding Distribution Systems

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.

Straight line detection from remote sensing images by rule-based feature fusion

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.

One airport detection method based on support vector machine

This paper proposes a novel airport detection method,which integrates the texture features and shape features of the airport.Eight texture features,such as the mean of the region,the deviation of the region,the smoothness of the region,the skewness of a histogram,the uniformity of the region,the randomness of the region,the mean of the gradient image and the deviation of the gradient image,are used to represent the features of the region.In this method,first the long lines are detected and the regions where the lines locate are segmented.Second,support vector machine(SVM)based on Gaussian kernel is used as a classifier which discriminates the runway from other candidate regions.Experimental results show that the error rate of the proposed method is lower than those of conventional methods which detect airport only by the shape feature of runway.The detection accuracy of the proposed method is nearly ten times higher than that of Liu’s methods,and the method has favorable speed for a real-time system.

Improving coherent averaging line spectrum detection with phase interpolation and compensation

For the purpose of resolving the problem of performance deterioration introduced by inaccurate phase compensation in existing coherent averaging line spectrum detectors, a modified coherent detector is proposed. The three point interpolation in frequency domain is applied to obtain accurate estimate of phase difference between segments when the segmented length is not an integral multiple of the signal period. Then the segmented data are multiplied by a complex coefficient to remove the phase difference and synchronize the phases of all the segments before coherent averaging. Theoretical analysis shows that there will be a gain of 3.9 dB at most by using the modified detector. The detection performance of the incoher- ent averaging power spectrum detector （AVGPR）, the phase coherent averaging detector, the modified coherent averaging detector are compared with each other by computer simulations. The results coincide basically with the theoretical analysis, which show the superiority of the modified detector to the former two detectors.

On-orbit real-time robust cooperative target identification in complex background

Cooperative target identification is the prerequisite for the relative position and orientation measurement between the space robot arm and the to-be-arrested object. We propose an on- orbit real-time robust algorithm for cooperative target identification in complex background using the features of circle and lines. It first extracts only the interested edges in the target image using an adaptive threshold and refines them to about single-pixel-width with improved non-maximum suppression. Adapting a novel tracking approach, edge segments changing smoothly in tangential directions are obtained. With a small amount of calculation, large numbers of invalid edges are removed. From the few remained edges, valid circular arcs are extracted and reassembled to obtain circles according to a reliable criterion. Finally, the target is identified if there are certain numbers of straight lines whose relative positions with the circle match the known target pattern. Experiments demonstrate that the proposed algorithm accurately identifies the cooperative target within the range of 0.3 1.5 m under complex background at the speed of 8 frames per second, regardless of lighting condition and target attitude. The proposed algorithm is very suitable for real-time visual measurement of space robot arm because of its robustness and small memory requirement.

Three-line structured light vision system for non-cooperative satellites in proximity operations

Adapter ring is a commonly used component in non-cooperative satellites,which has high strength and is suitable to be recognized and grasped by the space manipulator.During proximity operations,this circle feature may be occluded by the robot arm or limited field of view.Moreover,the captured images may be underexposed when there is not enough illumination.To address these problems,this paper presents a structured light vision system with three line lasers and a monocular camera.The lasers project lines onto the surface of the satellite,and six break points are formed along both sides of the adapter ring.A closed-form solution for real-time pose estimation is given using these break points.Then,a virtual structured light platform is constructed to simulate synthetic images of the target satellite.Compared with the predefined camera parameters and relative positions,the proposed method is demonstrated to be more effective,especially at a close distance.Besides,a physical space verification system is set up to prove the effectiveness and robustness of our method under different light conditions.Experimental results indicate that it is a practical and effective method for the pose measurement of on-orbit tasks.

Influence of computation algorithm on the accuracy of rut depth measurement

Rutting is one of the dominant pavement distresses, hence, the accuracy of rut depth measurements can have a substantial impact on the maintenance and rehabilitation （M 8： R） strategies and funding allocation. Different computation algorithms such as straight- edge method and wire line method, which are based on the same raw data, may lead to rut depth estimation which are not always consistent. Therefore, there is an urgent need to assess the impact of algorithm types on the accuracy of rut depth computation. In this paper, a 1B-point-based laser sensor detection technology, commonly accepted in China for rut depth measurements, was used to obtain a database of 85,000 field transverse profiles having three representative rutting shapes with small, medium and high severity rut levels. Based on the reconstruction of real transverse profiles, the consequences from two different algorithms were compared. Results showed that there is a combined effect of rut depth and profile shape on the rut depth computation accuracy. As expected, the dif- ference between the results obtained with the two computation methods increases with deeper rutting sections： when the distress is above 15 mm （severe level）, the average dif- ference between the two computation methods is above 1.5 mm, normally, the wire line method provides larger results. The computation suggests that the rutting shapes have a minimal influence on the results. An in-depth analysis showed that the upheaval outside of the wheel path is a dominant shape factor which results in higher computation differences.