最新刊期
- 摘要：With the rapid upgrading of smart cockpits and in-vehicle display technologies， automotive polarizers have become key optical materials affecting display performance and safety. Addressing the contradiction between the rapid development of China’s automotive display industry and the lack of dedicated standards for automotive polarizers， this paper aims to fill the research gap in standards， refine the theoretical framework of standard-driven industrial upgrading， resolve the industry pain points of domestic automotive polarizers such as “lack of standards to follow， difficulty in certification， slow industrialization process，” and improve the level of independent controllability of the industrial chain. Based on the application needs of all scenarios in automotive displays， this paper systematically analyzes the key bottlenecks between current standard system and the actual application of the automotive display industry， proposes the standard architecture and main indicators for automotive polarizers， and explores the implementation path for the large-scale application driven by standards. It puts forward the key points of polarizer technical standards adapted to all automotive display scenarios and forms a practical industrial application implementation plan. The research results can provide a unified basis for the research， design， production， manufacturing， and verification and evaluation of automotive polarizers. Through the deep integration of standardization and industrial application， it provides a theoretical basis and practical for the large-scale application of the automotive polarizer industry， which has important theoretical support and engineering application value for enhancing China’s independent guarantee capability of key materials for automotive displays， the security and stability of the industrial and supply chains， and achieving high-quality development.关键词：automotive display;polarizer;Standard;large-scale application31|11|0更新时间：2026-06-25
- 摘要：Aiming to address the challenges of missed detections and false positives in UAV aerial imagery caused by densely distributed small objects， small sizes， and complex backgrounds， this paper proposes an improved MCF-YOLO object detection algorithm based on YOLOv11n. First， a Multi-scale Feature Enhancement （MSFE） module is designed to enhance feature representation of small targets through high-resolution preservation， convolutional layer deepening， and residual connections. Second， a Contextual Anchor Attention （CAA） mechanism is integrated into the C3k2 module， leveraging horizontal and vertical separable convolutions to capture long-range spatial dependencies， thereby improving global contextual awareness and mitigating background interference and occlusion issues. Furthermore， the Focaler-DIoU loss function is employed to optimize the bounding box regression process through a dynamic sample weight adjustment mechanism， enhancing localization accuracy for small objects while reducing false positives and missed detections. Tests conducted on the VisDrone2019 dataset show that our method brings substantial performance enhancements relative to YOLOv11n， with increases of 6.8% in precision， 3.2% in recall， 6.6% in mAP@0.5， and 3.9% in mAP@0.5：0.95， outperforming other mainstream approaches. Additional cross-dataset validation on VEDAI and DOTA datasets shows mAP@0.5 improvements of 1.2% and 2.2% respectively， confirming the method's effectiveness and strong adaptability across various scenarios.关键词：deep learning;unmanned aerial vehicle （UAV）;small object detection;YOLOv11n;feature enhancement30|14|0更新时间：2026-06-25
- 摘要：The electroacoustic effect allows electrical control of acoustic waves through the converse piezoelectric effect， which is intrinsic to non‑centrosymmetric materials. Ferroelectric nematic liquid crystals （NF‑LCs） combine fluidity with spontaneous polarization， competing with traditional solid ferroelectrics. Here， we show that a confined ferroelectric nematic liquid crystal exhibits a pronounced electroacoustic response under an alternating electric field. The material emits audible sound only upon entering the ferroelectric nematic phase， with a sharp increase in recorded acoustic amplitude. Fourier analysis reveals the distinct underlying electromechanical coupling： the nonpolar nematic phase produces mainly even harmonics under weak electrostriction effect， whereas the polar NF phase manifests dominant odd harmonics with a low onset voltage （~2 V）， owing to a converse piezoelectric effect. The fundamental harmonic strongly dominates over the second harmonic， indicating a pronounced converse-piezoelectric contribution. The acoustic signal amplitude increases monotonically with cell thickness at a constant electric field， consistent with the piezoelectric displacement relation. The programmable operation is demonstrated by reproducing a melody by modulating the driving frequency over time while maintaining a constant voltage. This study presents fluid ferroelectric nematics as a platform for soft， reconfigurable acoustic transducers and sensors with low driving voltages and high piezoelectric responses.关键词：Ferroelectric nematic liquid crystal;electro-acoustic effect;harmonics;piezoelectricity;electrostriction23|12|0更新时间：2026-06-25
- 摘要：To address the issues of insufficient frequency information utilization and the limited capability to represent high-frequency details in complex distortion scenarios for no-reference image quality assessment （NR-IQA）， a frequency-aware quality assessment method guided by human visual system （HVS） characteristics is proposed. First， a lightweight hierarchical Vision Transformer network is employed as the backbone for multi-scale feature extraction， enabling information modeling from local textures to global semantics. Second， a frequency-aware gating module inspired by the contrast sensitivity function （CSF） is introduced. This module adaptively modulates distortion features across channel， frequency， and spatial dimensions to enhance the model’s perceptual sensitivity to sensitive frequency information. Subsequently， a high-frequency enhancement module based on the Discrete Cosine Transform （DCT） is designed， which strengthens the model's response to local high-frequency degradation through spatial and channel interactions in the frequency domain. Finally， multi-level features are aggregated to improve the collaborative representation capability among features. Experimental results demonstrate that the proposed method achieves a Pearson Linear Correlation Coefficient （PLCC） of 0.981 and a Spearman Rank-order Correlation Coefficient （SRCC） of 0.980 on the LIVE dataset. On the authentic distortion dataset LIVEC， the PLCC and SRCC reach 0.911 and 0.888， respectively， while the SRCC in the cross-dataset evaluation reaches 0.907. The proposed method effectively improves quality prediction accuracy in complex distortion scenarios， exhibits strong generalization and robustness， and can provide reliable support for perceptual quality optimization in intelligent visual systems.关键词：No-reference image quality assessment;contrast sensitivity function;frequency perception;discrete cosine transform;feature fusion21|12|0更新时间：2026-06-25
- 摘要：In space early warning and space target surveillance systems， aiming to effectively detect various space debris and other small-size， weak-signal celestial targets， it is necessary to address the challenges in deep space environments such as noise interference， faint target features and discontinuous trajectories. Therefore， a target detection and tracking algorithm based on improved YOLO26 is proposed. Firstly， an image sub-region self-perception enhancement module is deployed to enhance the visual clarity of dim and dark targets via local contrast optimization and edge preservation mechanism. Secondly， the YOLO26 detection network is lightweighted， and an improved multi-scale attention module GCEMA is introduced to strengthen the detection capability for dim and small targets. Finally， combined with the OCSort tracking algorithm and the appearance re-identification model CosmicReID， multi-target cataloging and stable tracking are realized. Experimental results show that compared with the original YOLO26 network and OCSort algorithm， the improved network has fewer parameters and faster inference speed， with the detection speed increased by 11.9%； the tracking accuracy of the integrated system is improved by 6.2%， and the frequency of target identity switching is reduced by 48.5%. The proposed method meets the requirements on detection accuracy and tracking accuracy of automatic tracking and measurement systems.关键词：multi-target tracking;image processing;object detection;data association25|26|0更新时间：2026-06-25
- 摘要：With the rapid advancement of urban intelligent transportation and autonomous driving， the multi-target detection at complex intersections faces issues such as easy missed detection of small targets， easy motion blur of high-speed moving targets， difficulty in recognizing long-distance occlusions， and imbalance in the training of easy and difficult samples. Existing algorithms are unable to simultaneously meet the dual requirements of high accuracy and real-time performance. This paper proposes Madulous， a two-branch traffic object detection algorithm based on the fusion of multiple attention mechanisms. Based on lightweight YOLOv8n， the DPFE dual-branch parallel feature extraction framework is constructed， and the main and auxiliary branches cooperate to extract features in the training phase. EMA efficient multi-scale attention module was embedded in the C2F module of the backbone network to realize the spatial and channel dual attention calibration. The Swin Transformer module was introduced into the neck network to assist the algorithm to fuse a wider range of context information. Then， the classification loss function of the model is reconstructed， and the training weight of difficult samples is adaptively increased based on IoU dynamic threshold. Comparative experiments show that the proposed algorithm mAP@0.5 reaches 91.5%， which is 2.5 percentage points higher than the benchmark YOLOv8n. The inference speed of EC-R3588SPC edge platform reaches 23.9 FPS， and the generalization performance is significantly better than the mainstream algorithms in traffic scenes such as fog and infrared aerial photography. Madulous algorithm achieves a good balance between detection accuracy and real-time performance， which can effectively support the real-time monitoring of traffic targets at intersections， and provides a reliable technical solution to reduce the incidence of vehicle and non-motor vehicle driving accidents.关键词：transportation information engineering and control;neural network;Transformer;multi-attention mechanism;object detection20|12|0更新时间：2026-06-25
- 摘要：Self-assembled microstructures in cholesteric liquid crystals， such as helices， dislocation lines， fingerprint textures， and topological solitons， have been widely studied and applied in the field of soft matter photonics. Among these， the precise construction of microstructures and their dynamic control using external fields have remained key research focuses. This review summarizes recent advances in the fabrication methods， stabilization conditions， and manipulation strategies for typical microstructures in cholesteric liquid crystals. It highlights techniques for controlling the orientation of helical axes and the evolution of fingerprint textures using optical， electric， and thermal fields. Furthermore， it introduces the application exploration of these microstructures in tunable gratings， reflective lenses， full-color displays， and particle manipulation. This review provides insights into achieving large-range precise control and functionalization of microstructures in cholesteric liquid crystals， and discusses their potential applications in emerging fields such as smart photonics and soft actuators， offering new possibilities for future research on cholesteric liquid crystal microstructures.关键词：cholesteric liquid crystals;helical structure;dislocation;fingerprint texture;optical applications;topological solitons;external-field manipulation31|15|0更新时间：2026-06-25
- 摘要：To solve the problems of low energy efficiency of small-pixel silicon-based chips and insufficient calculation accuracy of the scalar diffraction theory， this study conducts precise calculation and optimization research on the energy efficiency of silicon-based chips. The energy efficiency of silicon-based chips is calculated using both the scalar diffraction theory and the Finite-Difference Time-Domain （FDTD） method， and an experimental verification is carried out by building a 532 nm laser testing system. A λ/4-type multilayer dielectric high-reflection coating is designed using TiO₂/SiO₂ materials， and the optimization rules of the number of coating periods and operating wavelength on energy efficiency are analyzed via FDTD simulations. The research results show that for the currently common silicon-based chips with small pixel sizes （pixel period： 2~8 μm）， the absolute error between the energy efficiency calculated by the FDTD method and the experimental results is less than 1%. Coating the multilayer dielectric film can effectively improve the energy efficiency of the chip； when the number of reflective coating periods reaches 5， the energy efficiency of chips of various pixel sizes exceeds 95% in the wavelength range of 450~625 nm.关键词：silicon-based chip;diffraction efficiency;energy efficiency;finite-difference time-domain method;multilayer dielectric high-reflection coating103|23|0更新时间：2026-06-16
- 摘要：To address the challenges of complex interference and target occlusion in UAV target tracking， a Siamese network-based tracker named SiamAL is proposed， which incorporates an attention mechanism and an occlusion-discrimination module， along with its variant SiamAL-Det that additionally integrates an object detection module. Based on the SiamDMU algorithm， an attention mechanism is first introduced into the backbone network， and the h-swish activation function is adopted to enhance feature representation under complex interference conditions. A Kalman-filter-based motion prior is then introduced to estimate the target trajectory， and the tracking state is evaluated based on tracker confidence and prediction-observation residuals. In cases of severe occlusion， the motion prior weight is adaptively increased to maintain trajectory continuity. Finally， an integrated detection-and-tracking framework is designed to enhance tracking performance. A lightweight detection branch is triggered for re-detection and correction upon tracking failure or severe drift. Experimental results on the UAV123 dataset show that SiamAL achieves a success rate of 65.3% and a precision rate of 86.6%， while SiamAL-Det achieves 68.6% and 88.8%， respectively. In comparison with several mainstream Siamese network-based trackers， SiamAL outperforms in both success rate and precision， demonstrating the promising application prospects of the proposed method in UAV aerial tracking scenarios.关键词：UAV aerial imagery;siamese tracking;attention mechanism;Kalman filter35|21|0更新时间：2026-06-16
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Research on vehicle measurement and recognition technology based on GGW-YOLOv8 in binocular vision AI导读
摘要：Aiming at the problems of expensive and complex distance measurement sensors， low detection accuracy in complex road sections， and slow detection rate in traditional target detection and distance measurement technologies， a vehicle front distance measurement and target recognition technology based on GGW-YOLOv8 is proposed. This algorithm improves YOLOv8n by introducing GAM global attention mechanism before medium target and large target detection head， and improves the feature extraction ability of vehicle target by reducing information loss and amplifying global interaction. The GSConv lightweight convolutional module is introduced in the neck end network to reduce the number of model parameters and the calculation amount by deep separable convolution and shuffle operation. The WIoU_v3 loss function is used to replace the CIoU loss function， and the generalization ability and regression accuracy of the network boundary box are improved by the gradient gain distribution strategy of non-monootone dynamic focusing mechanism. The binocular vision ranging system based on SGBM semi-global stereo matching algorithm was built for vehicle ranging， and the upper computer system integrating vehicle target detection and ranging was developed， and transplanted and deployed to the embedded edge device for real vehicle on-board detection. The experimental results show that compared with the YOLOv8n model， the improved model has a 0.3M reduction in the number of parameters and 0.2G reduction in the amount of computation， and the accuracy of the self-made data set and the public data set is improved by 0.8% and 4.4%， respectively. In the closed section of the school， the absolute value of the average error is 104 mm， and the average relative error is 1.49%； in the real road， the absolute value of the average error is 307.4 mm， the average relative error is 3.29%， and the average time per frame is 30.12 ms， which can meet the needs of actual ranging scenarios.关键词：Vehicle target detection;YOLOv8;binocular ranging;embedded migration deployment38|21|0更新时间：2026-06-16 -
Atmospheric transmittance estimation for image dehazing using contrast and luminance similarity AI导读
摘要：To address the issues of low visibility in hazy images and dim brightness in dehazed results， a new transmittance estimation method based on contrast and luminance similarity is proposed for image dehazing. First， a differentiable image quality objective function is constructed using contrast and luminance similarity， from which a closed-form analytical solution for transmission is derived by maximizing the function. The transmission is then refined upward via a feedback mechanism. Next， a candidate atmospheric light generation method based on the dark channel prior and quadtree decomposition is introduced， which enhances the robustness of atmospheric light estimation by selecting the second-largest value from the candidate set. Finally， the haze-free image is recovered and enhanced using the atmospheric scattering model. Experimental results show that the proposed algorithm outperforms comparative methods across multiple image quality metrics. In terms of no-reference evaluation， it achieves superior mean performance in visible edge gradient ratio， saturated pixel ratio， and BRISQUE， with an average gradient ratio improvement of at least 11.0% and a reduction in saturated pixel ratio of at least 30.4%. For full-reference evaluation， the average PSNR is improved by at least 9.5% compared to similar algorithms. Moreover， the average runtime is reduced by no less than 12.76%. Overall， the proposed algorithm effectively enhances both image visibility and brightness while maintaining high computational efficiency.关键词：luminance similarity;contrast;image quality function;dark channel;quadtree algorithm;feedback mechanism21|21|0更新时间：2026-06-16 - 摘要：To address the issue of insufficient detection accuracy for small objects such as pedestrians in existing PointPillars-based frameworks， we propose an improved method that integrates sparse enhancement with geometric attention. First， we introduce a sparse Pillar enhancement mechanism during the Pillar feature encoding stage to fill in the gaps in geometric information in sparse regions， thereby providing effective material for subsequent feature enhancement. Second， we design a point-aware spatial attention mechanism embedded in the encoding process. Based on two types of geometric features of the completed Pillars， namely point cloud density and average point-to-center distance， we generate dynamic attention weights via a lightweight MLP to prioritize the amplification of feature signals from Pillars related to targets， thereby addressing the shortcomings of traditional attention mechanisms that rely on channel statistics and are ill-suited to point cloud characteristics. To validate the effectiveness of the method， experiments were conducted on the public KITTI 3D object detection dataset， with a focus on evaluating detection performance for small objects. The experimental results demonstrate that， compared to existing improved frameworks based on PointPillars， the average detection accuracy for pedestrian targets in 3D mode increases by 2.54%， and in bird’s-eye-view mode， it increases by 5.91%. This fully validates the effectiveness of the proposed innovative module in optimizing features for small targets and provides a practical solution for perception tasks in long-tail scenarios of autonomous driving.关键词：computer vision;3D object detection;attention mechanism;point cloud;light detection and ranging34|31|0更新时间：2026-05-29
- 摘要：Seed purity is a critical factor determining farmers’ harvests and income， and rapid， accurate variety identification technology is the core to ensuring purity. This study proposes a non-destructive detection method for maize seeds based on the fusion of LED white light imaging and the YOLOv8 model. Experiments confirm that this method combines the advantages of speed， efficiency， high accuracy， and non-destructiveness. By constructing an optimized LED white light reflection imaging system， we completed image acquisition and annotation for a total of 12，008 seeds from four maize varieties. Comparative experiments show that the YOLOv8m model significantly outperforms the lightweight YOLOv8s in various core metrics， effectively addressing the issue of insufficient identification accuracy for morphologically similar varieties. The model achieved an average precision （AP）， recall rate （AR）， and F1 score of 99.8% for the four varieties， with an average mAP@0.5 value as high as 99.4%. In real-time performance tests， the YOLOv8m model took 68.6 milliseconds to process a single image， fully meeting the low-latency requirements of seed sorting systems. The research also reveals the impact pattern of dataset scale on model performance， confirming that YOLOv8m maintains excellent stability across datasets of different scales. This study provides a precise and practical technical solution for the field of seed variety identification.关键词：maize seeds;variety Identification;deep learning;YOLOv828|24|0更新时间：2026-05-29
- 摘要：Liquid crystal optoelectronic devices face issues such as electrical safety hazards and poor portability. Conventional power supply devices feature high power consumption and reliance on external power sources， making it difficult to meet the demand for lightweight and self-powered operation. There is an urgent need for power supply devices that are safe， portable， low-power， and capable of utilizing renewable energy. This study aims to address these pain points by exploring novel self-powered driving strategies. Innovatively， moisture-enabled electric generators （MEGs） are integrated with polymer-dispersed liquid crystal （PDLC） gratings to construct a self-powered PDLC grating system. A 2×2 cm MEG is employed as the core energy supply component； its output performance of a single unit is tested， and series-parallel connection optimization is conducted to enhance power output for stable power supply to PDLC gratings. Driving and diffraction performance are evaluated using a conventional signal source as a control. A single 2×2 cm MEG yields an open-circuit voltage of 0.76 V and a short-ci rcuit current of 0.492 mA， and series-parallel configurations can boost power output to meet driving requirements. Its driving stability and response speed are highly comparable to those of the signal source. Moreover， under different supply voltages， the regulation law of diffraction efficiency of PDLC gratings driven by MEGs is completely consistent with that driven by signal sources， with the fluctuation error of diffraction efficiency controlled within 5%， verifying the reliability of the power supply. This study successfully proposes and implements a novel power supply scheme for liquid crystal devices centered on MEGs. It not only provides a green and portable self-powered solution for PDLC gratings， but also offers new ideas and technical references for the research on self-powered driving of various liquid crystal devices， which is of great significance for promoting the application of liquid crystal devices in flexible electronics， portable optoelectronic devices， and other fields.关键词：polymer dispersed liquid crystal;moisture-enabled electric generator;self-powered45|26|0更新时间：2026-05-29
- 摘要：To address the problems of large scale variation of defects， strong background interference and high complexity of detection models in UAV aerial road inspection， this paper proposes an improved detection model WADW-YOLO based on YOLOv11n. Firstly， a C3k2-WC weighted convolution module is proposed in the backbone network， which realizes spatial position weighting of convolution kernels through a predefined center-enhanced weight matrix， reducing the number of model parameters while focusing on feature extraction of defect regions. Secondly， a lightweight ADown downsampling module is adopted to improve the ability of retaining defect details， and a DAttention deformable attention mechanism is introduced at the end of the backbone to capture both global and local information by adaptively adjusting the receptive field. Finally， a Wise-ShapeIoU loss function is designed by combining the non-monotonic focusing mechanism of Wise-IoU and the geometric shape constraint of ShapeIoU to improve detection performance. WADW-YOLO achieves 74.6% mAP@0.5 on the public China_Drone dataset， which is 3.8% higher than that of YOLOv11n， with 17.8% fewer parameters and 23.8% less computation， achieving an effective balance between detection accuracy and model lightweightness. WADW-YOLO also obtains a 1.8% improvement in mAP@0.5 over YOLOv11n on the China_MotorBike dataset， demonstrating its outstanding generalization performance. This paper provides a technical reference for high-precision and low-power deployment schemes in UAV road defect detection.关键词：UAV;road defect;YOLOv11n;WADW-YOLO;lightweightness68|29|0更新时间：2026-05-29
- 摘要：With the rapid development of intelligent driving and digital twin technology， the efficient and real-time vehicle-road cooperative system has become a research focus. A digital twin smart car system based on improved YOLOv11_OBB-SVDD is designed in this paper. The system consists of five parts： physical smart car， virtual smart car， intelligent visualization service， twin data and dynamic real-time connection. Firstly， a physical smart car that can capture track images in real time through vision sensors to realize autonomous path recognition is constructed， and an industrial camera is deployed above the track to perceive the images of the smart car in the track. Then， a virtual model consistent with the physical smart car is established to enable bidirectional synchronization between the digital and physical domains. YOLOv11_OBB is used to detect the position of the physical smart car and the deflection angle of the body to realize the virtual-real mapping. To enhance the detection performance of small-sized smart cars in images， a frequency-aware feature fusion （FreqFusion） method is proposed to improve the model and enable accurate virtual-to-real mapping. Finally， the virtual smart car determines whether there is a risk of deviation from the track through the position of the digital twin by Support Vector Data Description （SVDD）， and sends control signals to the physical smart car using dynamic real-time connection. At the same time， the intelligent visualization service provides the visual dynamic monitoring of the smart car， and the multi-dimensional and multi-scale data of the digital twin smart car system are aggregated by twin data. Experimental results indicate that the proposed improvement of YOLOv11_OBB has achieved an average accuracy of 94.4%， 93.0%， and 92.2% respectively for the detection accuracy， recall rate， and mean average precision when the IOU threshold is 0.5 for the physical smart car. The SVDD-based anomaly detection system achieves an accuracy of 94.5%， a precision of 95.4%， and a recall of 93.5% in identifying both normal driving behavior and track deviation risks in smart car. The entire system operates smoothly and can accurately implement the functions of smart car autonomous tracking and digital twin modules..关键词：digital twin;smart car;improved YOLOv11_OBB;support vector data description25|25|0更新时间：2026-05-29
- 摘要：To address the problems of local texture and edge-detail loss， limited receptive fields in the convolution branch， and insufficient use of cross-layer structural information in the graph branch for hyperspectral image classification， this paper proposes a CNN-GCN joint classification model with high-low frequency enhancement and cross-layer graph convolution aggregation. The model improves input representation through high-low frequency residual enhancement， extracts multi-scale spectral-spatial features using a multi-stage dynamic convolution encoder， enhances regional structural modeling by cross-layer weighted aggregation in the graph branch， and performs collaborative modeling through cross-branch attention fusion. Experiments on three public datasets， Indian Pines， Pavia University， and Salinas， achieve overall accuracies of 92.94%， 95.11%， and 97.50%， with corresponding Kappa coefficients of 91.94%， 93.50%， and 97.22%， respectively. The results show that the proposed method can effectively integrate local details， spatial context， and regional topological structure information， achieving competitive classification performance across different types of hyperspectral classification scenes.关键词：Hyperspectral image classification;spectral-spatial joint features;graph convolution network;attention fusion179|201|0更新时间：2026-04-15
- 摘要：Infrared and visible image fusion aims to retain the complementary features of different modalities to achieve robust perception of complex scenes， and it plays a crucial role in numerous fields such as security monitoring， military reconnaissance， and autonomous driving. However， existing image fusion algorithms focus on enhancing the visual effects of images， leading to the ineffective preservation of key semantic information during the fusion process， which in turn affects the application performance of fused images in high-level visual tasks. Although existing methods attempt to cascade the fusion task with high-level visual tasks （segmentation， detection， etc.）， this sequential connection has limited enhancement on semantic information. To balance visual effects and downstream tasks， this paper proposes a semantic-driven infrared and visible image fusion network， SDFusion. First， a shared feature encoder is adopted to perform multi-level cross-modal feature extraction for infrared and visible images， fully mining the useful information in both modal images. Then， through the parallel collaborative optimization of the image fusion decoder and the semantic segmentation decoder， while hierarchically injecting encoded features into decoded features to enhance feature representation， the joint modeling of fusion features and semantic features is achieved. Experimental results on public datasets show that compared with traditional methods， this method significantly improves seven objective evaluation indicators. Specifically， EN is improved by 3.7%， SD by 7.3%， MI by 45.3%， VIFF by 18.5%， and
- 摘要：Aiming at the problem that the stitching of borehole wall images in geological exploration is easily affected by multiple factors such as imaging illumination conditions， probe disturbance， and information missing of depth or azimuth， a robust stitching method for borehole wall images under multi-interference imaging conditions is proposed. Firstly， an image mapping method with conditional constraints is adopted to expand a specific sampling region of the borehole wall image into a narrowband image that can be effectively used for stitching under the interference of the imaging light source. Then， the intrinsic characteristics of the images in borehole video are utilized， and an ROI-based registration strategy is adopted to realize the accurate registration of the unfolded narrowband borehole images under the conditions of probe disturbance and information missing of depth and azimuth， and then a precise registration of the unfolded narrowband image of the borehole under the conditions of probe disturbance， missing depth or azimuthal angle. At the same time， a grid-based matching strategy is applied to improve the registration speed. Finally， a row-by-row dynamically weighted image fusion method is adopted to eliminate the stitching traces according to the registration results， which further optimizes the quality of the stitched images. Through the experiments on the simulated images under different geological conditions， the performance indexes of the proposed method with average value of SSIM of 71.39%， average value of PSNR of 26.74， and average value of registration accuracy of 92.38% are better than those of the comparison method. and through the experimental results on the real images of different borehole videos， the objective evaluation indexes of the stitching results obtained by the proposed method， such as EN， SF， AG， MI， and Q_MI， etc.， are all improved.关键词：image stitching;image registration;image fusion;borehole wall image208|391|0更新时间：2025-02-25
- 摘要：Manganese （Mn2+） ion-doped all-inorganicCsPbCl3 perovskite quantum dots have shown great potential for application in Mini/Micro-LED display devices due to their unique photoluminescence properties and nano-scale particle size. However， the current photoluminescence quantum yields of CsPbCl3：Mn2+ perovskite quantum dots is low and cannot meet the needs of practical applications. Herein， the potassium （K+） and Mn2+ ions co-doped CsPbCl3 perovskite quantum dots are synthesized via the hot injection method. The prepared CsPbCl3：（K+， Mn2+） perovskite quantum dots present a dual-color emission， which can be assigned to the perovskite excitons emission and Mn2+ ions emission. The photoluminescence quantum yield of CsPbCl3：（K+， Mn2+） perovskite quantum dots is improved from 16.5% of original to 66.2% through the optimization of K+ ions doping concentration. Meanwhile， the regulation mechanism of K+ ions doping on the photoluminescence properties of CsPbCl3：Mn2+ perovskite quantum dots are investigated. It is shown that the incorporation of K+ ions can effectively inhibit the formation of intrinsic defect states and Mn-Mn dimers or Mn-related defects in CsPbCl3：（K+， Mn2+） perovskite quantum dots， thus enhancing the radiative recombination luminescence of carriers in perovskite quantum dots. Benefiting from the strategy of hetero-ions doping， these doped perovskite quantum dots are expected to be applied in Mini/Micro-LED display fields.关键词：CsPbCl3： Mn2+ perovskite quantum dots;K+ ions doping;dual-color emission;photoluminescence quantum yields234|350|0更新时间：2024-09-04
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