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.

Research on Transmission Line Tower Tilting and Foundation State Monitoring Technology Based onMulti-Sensor Cooperative Detection and Correction

The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission.The tower’s tilt and severe deformation will cause the building to collapse.Many small changes caused the tower’s collapse,but the early staff often could not intuitively notice the changes in the tower’s state.In the current tower online monitoring system,terminal equipment often needs to replace batteries frequently due to premature exhaustion of power.According to the need for real-time measurement of power line tower,this research designed a real-time monitoring device monitoring the transmission tower attitude tilting and foundation state based on the inertial sensor,the acceleration of 3 axis inertial sensor and angular velocity raw data to pole average filtering pre-processing,and then through the complementary filtering algorithm for comprehensive calculation of tilt angle,the system meets the demand for inclined online monitoring of power line poles and towers regarding measurement accuracy,with low cost and power consumption.The optimization multi-sensor cooperative detection and correction measured tilt angle result relative accuracy can reach 1.03%,which has specific promotion and application value since the system has the advantages of unattended and efficient calculation.

Analysis of the joint detection capability of the SMILE satellite and EISCAT-3D radar

The Solar wind Magnetosphere Ionosphere Link Explorer(SMILE)satellite is a small magnetosphere–ionosphere link explorer developed cooperatively between China and Europe.It pioneers the use of X-ray imaging technology to perform large-scale imaging of the Earth’s magnetosheath and polar cusp regions.It uses a high-precision ultraviolet imager to image the overall configuration of the aurora and monitor changes in the source of solar wind in real time,using in situ detection instruments to improve human understanding of the relationship between solar activity and changes in the Earth’s magnetic field.The SMILE satellite is scheduled to launch in 2025.The European Incoherent Scatter Sciences Association(EISCAT)-3D radar is a new generation of European incoherent scatter radar constructed by EISCAT and is the most advanced ground-based ionospheric experimental device in the high-latitude polar region.It has multibeam and multidirectional quasi-real-time three-dimensional(3D)imaging capabilities,continuous monitoring and operation capabilities,and multiple-baseline interferometry capabilities.Joint detection by the SMILE satellite and the EISCAT-3D radar is of great significance for revealing the coupling process of the solar wind–magnetosphere–ionosphere.Therefore,we performed an analysis of the joint detection capability of the SMILE satellite and EISCAT-3D,analyzed the period during which the two can perform joint detection,and defined the key scientific problems that can be solved by joint detection.In addition,we developed Web-based software to search for and visualize the joint detection period of the SMILE satellite and EISCAT-3D radar,which lays the foundation for subsequent joint detection experiments and scientific research.

A self-organization formation configuration based assignment probability and collision detection

The formation control of multiple unmanned aerial vehicles(multi-UAVs)has always been a research hotspot.Based on the straight line trajectory,a multi-UAVs target point assignment algorithm based on the assignment probability is proposed to achieve the shortest overall formation path of multi-UAVs with low complexity and reduce the energy consumption.In order to avoid the collision between UAVs in the formation process,the concept of safety ball is introduced,and the collision detection based on continuous motion of two time slots and the lane occupation detection after motion is proposed to avoid collision between UAVs.Based on the idea of game theory,a method of UAV motion form setting based on the maximization of interests is proposed,including the maximization of self-interest and the maximization of formation interest is proposed,so that multi-UAVs can complete the formation task quickly and reasonably with the linear trajectory assigned in advance.Finally,through simulation verification,the multi-UAVs target assignment algorithm based on the assignment probability proposed in this paper can effectively reduce the total path length,and the UAV motion selection method based on the maximization interests can effectively complete the task formation.

An Implementation of Multiscale Line Detection and Mathematical Morphology for Efficient and Precise Blood Vessel Segmentation in Fundus Images

Diagnosing various diseases such as glaucoma,age-related macular degeneration,cardiovascular conditions,and diabetic retinopathy involves segmenting retinal blood vessels.The task is particularly challenging when dealing with color fundus images due to issues like non-uniformillumination,low contrast,and variations in vessel appearance,especially in the presence of different pathologies.Furthermore,the speed of the retinal vessel segmentation system is of utmost importance.With the surge of now available big data,the speed of the algorithm becomes increasingly important,carrying almost equivalent weightage to the accuracy of the algorithm.To address these challenges,we present a novel approach for retinal vessel segmentation,leveraging efficient and robust techniques based on multiscale line detection and mathematical morphology.Our algorithm’s performance is evaluated on two publicly available datasets,namely the Digital Retinal Images for Vessel Extraction dataset(DRIVE)and the Structure Analysis of Retina(STARE)dataset.The experimental results demonstrate the effectiveness of our method,withmean accuracy values of 0.9467 forDRIVE and 0.9535 for STARE datasets,aswell as sensitivity values of 0.6952 forDRIVE and 0.6809 for STARE datasets.Notably,our algorithmexhibits competitive performance with state-of-the-art methods.Importantly,it operates at an average speed of 3.73 s per image for DRIVE and 3.75 s for STARE datasets.It is worth noting that these results were achieved using Matlab scripts containing multiple loops.This suggests that the processing time can be further reduced by replacing loops with vectorization.Thus the proposed algorithm can be deployed in real time applications.In summary,our proposed system strikes a fine balance between swift computation and accuracy that is on par with the best available methods in the field.

A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.

Multi-Equipment Detection Method for Distribution Lines Based on Improved YOLOx-s

The YOLOx-s network does not sufficiently meet the accuracy demand of equipment detection in the autonomous inspection of distribution lines by Unmanned Aerial Vehicle(UAV)due to the complex background of distribution lines,variable morphology of equipment,and large differences in equipment sizes.Therefore,aiming at the difficult detection of power equipment in UAV inspection images,we propose a multi-equipment detection method for inspection of distribution lines based on the YOLOx-s.Based on the YOLOx-s network,we make the following improvements:1)The Receptive Field Block(RFB)module is added after the shallow feature layer of the backbone network to expand the receptive field of the network.2)The Coordinate Attention(CA)module is added to obtain the spatial direction information of the targets and improve the accuracy of target localization.3)After the first fusion of features in the Path Aggregation Network(PANet),the Adaptively Spatial Feature Fusion(ASFF)module is added to achieve efficient re-fusion of multi-scale deep and shallow feature maps by assigning adaptive weight parameters to features at different scales.4)The loss function Binary Cross Entropy(BCE)Loss in YOLOx-s is replaced by Focal Loss to alleviate the difficulty of network convergence caused by the imbalance between positive and negative samples of small-sized targets.The experiments take a private dataset consisting of four types of power equipment:Transformers,Isolators,Drop Fuses,and Lightning Arrestors.On average,the mean Average Precision(mAP)of the proposed method can reach 93.64%,an increase of 3.27%.The experimental results show that the proposed method can better identify multiple types of power equipment of different scales at the same time,which helps to improve the intelligence of UAV autonomous inspection in distribution lines.

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.

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.

Upper oesophageal images and Z-line detection with 2 different small-bowel capsule systems

Transmission of oesophageal images may vary between different small-bowel capsule endoscopy models. A retrospective review of 100 examinations performed with 2 different Small-bowel capsule endoscopy （SBCE） sys- tems （PillCam and MiroCam） was performed. The oral cavity/aero-digestive tract （i.e., tongue, uvula and/or epiglottis） was captured/identified in almost all （99%） of PillCam videos but in none of MiroCam cases, P 〈 0.0001. Furthermore, oesophageal images （i.e., from the upper oesophageal sphincter to the Z-line were cap- tured in 99% of PillCam videos （mean =1= SD, 60.5 ± 334.1 frames, range： 0-3329 frames） and in 66% of Mi- roCam cases （mean ± SD, 11.1 ± 46.5 frames, range： 0-382 frames）, P 〈 0.0001. The Z-line was identified in 42% of PilICam videos and 17% of MiroCam, P = 0.0002. This information might be useful when perform- ing SBCE in patients with high risks for aspiration.

Transfer and Detection of barstar Gene to Maize Inbred Line 18-599 (White) by Particle Bombardment

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.

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.

LDNet:structure-focused lane detection based on line deformation

Lane detection is a fundamental necessary task for autonomous driving.The conventional methods mainly treat lane detection as a pixel-wise segmentation problem,which suffers from the challenge of uncontrollable driving road environments and needs post-processing to abstract the lane parameters.In this work,a series of lines are used to represent traffic lanes and a novel line deformation network(LDNet) is proposed to directly predict the coordinates of lane line points.Inspired by the dynamic behavior of classic snake algorithms,LDNet uses a neural network to iteratively deform an initial lane line to match the lane markings.To capture the long and discontinuous structures of lane lines,1 D convolution in LDNet is used for structured feature learning along the lane lines.Based on LDNet,a two-stage pipeline is developed for lane marking detection:(1) initial lane line proposal to predict a list of lane line candidates,and(2) lane line deformation to obtain the coordinates of lane line points.Experiments show that the proposed approach achieves competitive performances on the TuSimple dataset while being efficient for real-time applications on a GTX 1650 GPU.In particular,the accuracy of LDNet with the annotated starting and ending points is up to99.45%,which indicates the improved initial lane line proposal method can further enhance the performance of LDNet.

PRECONCENTRATION OF TRACE ZINC IN SEAWATER ON CPPI RESIN BY FIA AND ON-LINE DETECTION BY ICP-AFS

A quite new type of chelating resin Carboxymethylated Polyethylenimine-Polymethylenepolyphenylene Isocyanate(CPPI)is used for the preconcentration of Zn from high salt water such as seawater. The preconcentration is controlled through the technique of Flow Injection Analysis(FIA).The concentrated sample solution is then directly transferred to an Inductively Coupled Plasma-Atomic Fluorescence Spectrometer(ICP-AFS)for determination.The detection limit of Zn by the technique is about 0.06 ppb.

Disparity-Based Generation of Line-of-Sight DSM for Image-Elevation Co-Registration to Support Building Detection in Off-Nadir VHR Satellite Images

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.

On-line Chatter Detection Using an Improved Support Vector Machine

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.

On-Line Detection of EDM Spark Locations Using Potential Difference Method

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.

Microfluidic Analytical System with On-Line Luminol Chemiluminescence Detection Based on Annular Flow of Phase Separation Multiphase Flow

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.

基于改进YOLOv5-LITE轻量级的配电组件缺陷识别

为对配电组件缺陷进行精确快速的定位和识别,提出一种基于改进YOLOv5-LITE轻量级的配电组件缺陷识别方法。为使模型便于部署至移动设备终端,该方法使用ShuffleNetV2作为骨干网提取特征构建YOLOv5-LITE轻量级神经网络模型,并摘除ShuffleNetV2的1024卷积和5×5池化,采用全局平均池化操作替代,降低网络参数量,提升模型检测速度;通过引入有利于细粒度目标检测的152×152特征层,实现了对大、中、小尺度的缺陷检测;在PANet架构中采用深度可分离卷积代替下采样使得网络更加轻量化。实验结果表明:该方法能够识别电缆脱离垫片、电缆与绝缘子脱落、无环绝缘子3种缺陷,其检测精度分别达到92%、95%、95%,网络参数量约为YOLOv5的1/4,检测速度达到2 ms/张。所提出的方法具有实时性、准确率高、轻量化等特点。

基于先验知识辅助聚类的粗-精输电线路多金具检测

为了解决输电线路多金具检测任务中存在的小目标和密集遮挡问题并充分利用金具高分辨率航拍图像的信息优势,提出基于先验知识辅助聚类的粗-精输电线路多金具检测方法。首先,通过粗检测模块实现对输电线路高分辨率航拍图像的初步感知。接着,通过先验知识指导结构场景子区域选取模块中聚类算法半径的确定,以自适应聚类出合适的子区域。最后,设计精检测模块充分利用高分辨率航拍图像中的关键信息,进行金具的精确感知,并融合粗检测结果以实现由粗到精的金具识别。经实验证明,基于先验知识辅助聚类的粗-精输电线路多金具检测模型比之基线模型准确率提高了11.3%,对其中小目标金具和密集遮挡金具检测准确率的提高尤为明显。