Search Results (200)

The article addresses the challenges of beam position tracking in Free-Space Optical Communication (FSOC) systems. A review of available photodetector technologies is presented, highlighting their operating principles and applications in optical links. The analysis indicates that most current monitoring devices function with the visible and near- or short-infrared ranges. However, due to the propagation characteristics of radiation in terrestrial environments, the mid-wave infrared (MWIR) region offers particularly promising opportunities. To the end, the work introduces a novel detector module based on an MWIR quadrant detector capable of simultaneously performing two essential tasks: monitoring beam position and receiving transmitted data. Such an integrated approach has the potential to significantly simplify the design of mobile FSOC systems, especially those requiring accurate transceivers’ tracking. The concept was validated through laboratory experiments on an MWIR link model, where both the signal bandwidth and position transfer function of the quadrant detector were examined. Full article

Accurate and efficient detection of underwater targets in sonar imagery is critical for applications such as marine exploration, infrastructure inspection, and autonomous navigation. However, sonar-based object detection remains challenging due to low resolution, high noise, cluttered backgrounds, and the scarcity of annotated data. To address these issues, we propose UWS-YOLO, a novel detection framework specifically designed for underwater sonar images. The model integrates three key innovations: (1) a C2F-Ortho module that enhances multi-scale feature representation through orthogonal channel attention, improving sensitivity to small and low-contrast targets; (2) a DySnConv module that employs Dynamic Snake Convolution to adaptively capture elongated and irregular structures such as pipelines and cables; and (3) a cross-modal transfer learning strategy that pre-trains on large-scale optical underwater imagery before fine-tuning on sonar data, effectively mitigating overfitting and bridging the modality gap. Extensive evaluations on real-world sonar datasets demonstrate that UWS-YOLO achieves a mAP@0.5 of 87.1%, outperforming the YOLOv8n baseline by 3.5% and seven state-of-the-art detectors in accuracy while maintaining real-time performance at 158 FPS with only 8.8 GFLOPs. The framework exhibits strong generalization across datasets, robustness to noise, and computational efficiency on embedded devices, confirming its suitability for deployment in resource-constrained underwater environments. Full article

As the core component of photovoltaic (PV) power generation systems, PV cells are susceptible to subtle surface defects, including thick lines, cracks, and finger interruptions, primarily caused by stress and material brittleness during the manufacturing process. These defects substantially degrade energy conversion efficiency by inducing both optical and electrical losses, yet existing detection methods struggle to precisely identify and localize them. In addition, the complexity of background noise and other factors further increases the challenge of detecting these subtle defects. To address these challenges, we propose a novel PV Cell Surface Defect Detector (PSDD) that extracts subtle defects both within the backbone network and during feature fusion. In particular, we propose a plug-and-play Subtle Feature Refinement Module (SFRM) that integrates into the backbone to enhance fine-grained feature representation by rearranging local spatial features to the channel dimension, mitigating the loss of detail caused by downsampling. SFRM further employs a general attention mechanism to adaptively enhance key channels associated with subtle defects, improving the representation of fine defect features. In addition, we propose a Background Noise Suppression Block (BNSB) as a key component of the feature aggregation stage, which employs a dual-path strategy to fuse multiscale features, reducing background interference and improving defect saliency. Specifically, the first path uses a Background-Aware Module (BAM) to adaptively suppress noise and emphasize relevant features, while the second path adopts a residual structure to retain the original input features and prevent the loss of critical details. Experiments show that PSDD outperforms other methods, achieving the highest $mA{P}_{50}$ scores of 93.6% on the PVEL-AD. Full article

This work reports the synthesis and characterization of Carbon Quantum Dots (CQDs) embedded in an organic–inorganic hybrid SiO₂: PMMA matrix, designed as a novel plastic scintillator material. The CQDs were synthesized through a solvo-hydrothermal method and incorporated using a sol–gel polymerization process, resulting in a mechanically durable and optically active hybrid. Structural analysis with X-ray diffraction and TEM confirmed crystalline quantum dots approximately 10 nm in size. Extensive optical characterization, including band gap measurement, photoluminescence under 325 nm UV excitation, lifetime evaluations, and quantum yield measurement, revealed a blue emission centered at 426 nm with a decay time of 3–3.6 ns. The hybrid scintillator was integrated into a compact cosmic ray detector using a photomultiplier tube optimized for 420 nm detection. The system effectively detected secondary atmospheric muons produced by low-energy cosmic rays, validated through the vertical equivalent muon (VEM) technique. These findings highlight the potential of CQD-based hybrid materials for advanced optical sensing and scintillation applications in complex environments, supporting the development of compact and sensitive detection systems. Full article

From the point of view of the intelligent operation and maintenance of high-speed train tracks, this paper examines the research status of high-speed train rail damage detection technology in the field of high-speed train track operation and maintenance detection in recent years, summarizes the damage detection methods for high-speed trains, and compares and analyzes different detection technologies and application research results. The analysis results show that the detection methods for high-speed train rail damage mainly focus on the research and application of non-destructive testing technology and methods, as well as testing platform equipment. Detection platforms and equipment include a new type of vortex meter, integrated track recording vehicles, laser rangefinders, thermal sensors, laser vision systems, LiDAR, new ultrasonic detectors, rail detection vehicles, rail detection robots, laser on-board rail detection systems, track recorders, self-moving trolleys, etc. The main research and application methods include electromagnetic detection, optical detection, ultrasonic guided wave detection, acoustic emission detection, ray detection, vortex detection, and vibration detection. In recent years, the most widely studied and applied methods have been rail detection based on LiDAR detection, ultrasonic detection, eddy current detection, and optical detection. The most important optical detection method is machine vision detection. Ultrasonic detection can detect internal damage of the rail. LiDAR detection can detect dirt around the rail and the surface, but the cost of this kind of equipment is very high. And the application cost is also very high. In the future, for high-speed railway rail damage detection, the damage standards must be followed first. In terms of rail geometric parameters, the domestic standard (TB 10754-2018) requires a gauge deviation of ±1 mm, a track direction deviation of 0.3 mm/10 m, and a height deviation of 0.5 mm/10 m, and some indicators are stricter than European standard EN-13848. In terms of damage detection, domestic flaw detection vehicles have achieved millimeter-level accuracy in crack detection in rail heads, rail waists, and other parts, with a damage detection rate of over 85%. The accuracy of identifying track components by the drone detection system is 93.6%, and the identification rate of potential safety hazards is 81.8%. There is a certain gap with international standards, and standards such as EN 13848 have stricter requirements for testing cycles and data storage, especially in quantifying damage detection requirements, real-time damage data, and safety, which will be the key research and development contents and directions in the future. Full article

Silicon Photomultipliers (SiPMs) are optical sensors widely used in space applications due to their high photon detection efficiency, low power consumption, and robustness. However, in Low Earth Orbit (LEO), their performance degrades over time due to prolonged exposure to ionizing radiation, primarily from trapped protons and electrons. The dominant radiation-induced effect in SiPMs is an increase in dark current, which can compromise detector sensitivity. This study investigates the potential of thermal annealing as a mitigation strategy for radiation damage in SiPMs. We designed and tested PCB-integrated heaters to selectively heat irradiated SiPMs and induce recovery processes. A PID-controlled system was developed to stabilize the temperature at 100 °C, and a remotely controlled experimental setup was implemented to operate under irradiation conditions. Two SiPMs were simultaneously irradiated with 9 MeV protons at the EDRA facility, reaching a 1 MeV neutron equivalent cumulative fluence of (9.5 ± 0.2) × 10⁸ cm⁻². One sensor underwent thermal annealing between irradiation cycles, while the other served as a control. Throughout the experiment, dark current was continuously monitored using a source measure unit, and I–V curves were recorded before and after irradiation. A recovery of more than 39% was achieved after only 5 min of thermal cycling at 100 °C, supporting this recovery approach as a low-complexity strategy to mitigate radiation-induced damage in space-based SiPM applications and increase device lifetime in harsh environments. Full article

We present THz direct detectors based on an AlGaN/GaN high electron mobility transistor (HEMT), featuring excellent optical sensitivity and low noise-equivalent power (NEP). These detectors are monolithically integrated with various antenna designs and exhibit state-of-the-art performance at room temperature. Their architecture enables straightforward scaling to two-dimensional formats, paving the way for terahertz focal plane arrays (FPAs). In particular, for one detector type, a fully realized THz FPA has been demonstrated in this paper. Theoretical and experimental characterizations are provided for both single-pixel detectors (0.1–1.5 THz) and the FPA (0.1–1.1 THz). The broadband single detectors achieve optical sensitivities exceeding 20 mA/W up to 1 THz and NEP values below 100 pW/$\sqrt{\mathrm{Hz}}$. The best optical NEP is below 10 pW/$\sqrt{\mathrm{Hz}}$ at 175 GHz. The reported sensitivity and NEP values were achieved including antenna and optical coupling losses, underlining the excellent overall performance of the detectors. Full article

This article presents an innovative integration of Glow Discharge Detector Focal Plane Arrays (GDD FPA) with Chirped Frequency Modulated Continuous Wave (FMCW) Radar, enhancing millimeter-wave (MMW) imaging. The cost-effective FPA design using GDDs as pixel elements forms the foundation of the system. We investigate MMW effects on GDD discharge currents via basic data acquisition (DAQ) and implement a scanning mechanism with a step motor for sub-pixel imaging. The setup integrates an MMW source, optical components, a timer/counter, and an 8 × 8 FPA with 64 GDD, operating in electrical detection modes and processing signals using Fast Fourier Transform (FFT) algorithms. Recent advancements in millimeter-wave imaging have focused on improving image resolution and acquisition speed through various techniques, including lock-in amplifiers and electrical detection methods. However, these methods introduce complexity, cost, and extended acquisition times. Our approach mitigates these challenges by implementing a simplified FPA design that eliminates the need for external signal conditioning elements, providing faster and more efficient image acquisition. The primary contributions include significant improvements in the speed and automation of image acquisition achieved through a coordinated control mechanism for efficient row scanning. Compared to previous generations of GDD FPAs, this system achieves a notable reduction in image acquisition time by up to 75%, while maintaining high fidelity. These enhancements make the system particularly suitable for time-sensitive applications. Additionally, future research directions include the incorporation of 3D imaging using FMCW radar. Results from the FMCW measurements using the single GDD circuit demonstrate the system’s ability to accurately capture and process MMW radiation, even at low intensities. The combined strengths of GDD FPA and chirped FMCW radar underscore the system’s effectiveness in MMW detection, laying the groundwork for advanced MMW imaging capabilities across diverse applications. Full article

As a typical visual foundation model, SAM has been extensively utilized for optical image segmentation tasks. However, synthetic aperture radar (SAR) employs a unique imaging mechanism, and its images are very different from optical images. Directly transferring a pretrained SAM from optical scenes to SAR image instance segmentation tasks can lead to a substantial decline in performance. Therefore, this paper fully integrates the SAR scattering mechanism, and proposes a SAR image target segmentation method guided by the SAR scattering mechanism-based visual foundation model. First, considering the discrete distribution features of strong scattering points in SAR imagery, we develop an edge enhancement morphological adaptor. This adaptor is designed to incorporate a limited set of trainable parameters aimed at effectively boosting the target’s edge morphology, allowing quick fine-tuning within the SAR realm. Second, an adaptive denoising module based on wavelets and soft-thresholding techniques is implemented to reduce the impact of SAR coherent speckle noise, thus improving the feature representation performance. Furthermore, an efficient automatic prompt module based on a deep object detector is built to enhance the ability of rapid target localization in wide-area scenes and improve image segmentation performance. Our approach has been shown to outperform current segmentation methods through experiments conducted on two open-source datasets, SSDD and HRSID. When the ground-truth is used as a prompt, SARSAM improves $mIOU$ by more than 10%, and $A{P}_{\mathrm{mask}}^{50}$ by more than 5% from the baseline. In addition, the computational cost is greatly reduced because the number of parameters and FLOPs of the structures that require fine-tuning are only 13.5% and 10.1% of the baseline, respectively. Full article

Synthetic Aperture Radar (SAR) aircraft detection plays a crucial role in various civilian applications. Benefiting from the powerful capacity of feature extraction and analysis of deep learning, aircraft detection performance has been improved by most traditional general-purpose visual intelligence methods. However, the inherent imaging mechanisms of SAR fundamentally differ from optical images, which poses challenges for SAR aircraft detection. Aircraft targets in SAR imagery typically exhibit indistinct details, discrete features, and weak contextual associations and are prone to non-target interference, which makes it difficult for existing visual detectors to capture critical features of aircraft, limiting further optimization of their performance. To address these issues, we propose the scattering feature guidance network (SFG-Net), which integrates feature extraction, global feature fusion, and label assignment with essential scattering distribution of targets. This enables the network to focus on critical discriminative features and leverage robust scattering features as guidance to enhance detection accuracy while suppressing interference. The core components of the proposed method include the detail feature supplement (DFS) module and the context-aware scattering feature enhancement (CAFE) module. The former integrates low-level texture and contour features to mitigate detail ambiguity and noise interference, while the latter leverages global context of strong scattering information to generate more discriminative feature representations, guiding the network to focus on critical scattering regions and improving learning of essential features. Additionally, a feature scattering center-based label assignment (FLA) strategy is introduced, which utilizes the spatial distribution of scattering information to adaptively adjust the sample coverage and ensure that strong scattering regions are prioritized during training. A series of experiments was conducted on the CSAR-AC dataset to validate the effectiveness and generalizability of the proposed method. Full article

Visual odometry (VO), including keypoint detection, correspondence establishment, and pose estimation, is a crucial technique for determining motion in machine vision, with significant applications in augmented reality (AR), autonomous driving, and visual simultaneous localization and mapping (SLAM). For feature-based VO, the repeatability of keypoints affects the pose estimation. The convolutional neural network (CNN)-based detectors extract high-level features from images, thereby exhibiting robustness to viewpoint and illumination changes. Compared with descriptor matching, optical flow tracking exhibits better real-time performance. However, mainstream CNN-based detectors rely on the “joint detection and descriptor” framework to realize matching, making them incompatible with optical flow tracking. To obtain keypoints suitable for optical flow tracking, we propose a self-supervised detector based on transfer learning named OFPoint, which jointly calculates pixel-level positions and confidences. We use the descriptor-based detector simple learned keypoints (SiLK) as the pre-trained model and fine-tune it to avoid training from scratch. To achieve multi-scale feature fusion in detection, we integrate the multi-scale attention mechanism. Furthermore, we introduce the maximum discriminative probability loss term, ensuring the grayscale consistency and local stability of keypoints. OFPoint achieves a balance between accuracy and real-time performance when establishing correspondences on HPatches. Additionally, we demonstrate its effectiveness in VO and its potential for graphics applications such as AR. Full article

Circular polarization detection plays a significant role in various fields, such as optical communication, quantum information processing, biomedical detection, polarization imaging, and sensing. Traditionally, similar to linear polarization detection, circular polarization detection is realized by systems composed of discrete optical components, which tend to be bulky and complex. With the advancement of technology, there is a growing demand for efficient and compact integrated circular polarization detectors. In this review, we focus on two advanced research areas concerning integrated circular polarization detectors: those based on asymmetric materials or structures. We explore recent advances and future prospects and challenges for the development of integrated circular polarization detectors. Full article

In this study, the luminescence efficiency of a crystal-form barium fluoride (BaF₂) inorganic scintillator was assessed for medical imaging applications. For the experiments, we used a typical medical X-ray tube (50–140 kVp) for estimating the absolute luminescence efficiency (AE). Furthermore, we examined the spectral matching of the inorganic scintillator with a series of optical detectors. BaF₂ showed a higher AE than cerium fluoride (CeF₃), comparable to that of commercially available bismuth germanate (Bi₄Ge₃O₁₂-BGO), but lower than that of the gadolinium orthosilicate (Gd₂SiO₅:Ce-GSO:Ce) inorganic scintillator. The maximum AE of BaF₂ was 2.36 efficiency units (EU is the S.I. equivalent μWm⁻²/(mR/s) at 140 kVp, which is higher than that of the corresponding fluoride-based CeF₃ (0.8334 EU)) at the same X-ray energy. GSO:Ce and BGO crystals, which are often integrated in commercial positron emission tomography (PET) scanners, had AE values of 7.76 and 3.41, respectively. The emission maximum (~310 nm) of BaF₂ is adequate for coupling with flat-panel position-sensitive (PS) photomultipliers (PMTs) and various photocathodes. The luminescence efficiency results of BaF₂ were comparable to those of BGO; thus, it could possibly be used in medical imaging modalities, considering its significantly lower cost. Full article

A wide band range can cover more of the characteristic spectral lines of substances, and thus analyze the structure and composition of substances more accurately. In order to broaden the band range of spectral instruments, an ultra-wide-band Fourier transform infrared spectrometer is designed. The incident light of the spectrometer is constrained by a secondary imaging scheme, and switchable light sources and detectors are set to achieve an ultra-wide band coverage. A compact and highly stable double-moving mirror swing interferometer is adopted to generate optical path difference, and a controller is used to stabilize the swing of the moving mirrors. A distributed design of digital system integration and analog system integration is adopted to achieve a lightweight and low-power-consumption spectrometer. High-speed data acquisition and a transmission interface are applied to improve the real-time performance. Further, a series of experiments are performed to test the performance of the spectrometer. Finally, the experimental results show that the spectral range of the ultra-wide-band Fourier transform infrared spectrometer covers 0.770–200 μm, with an accurate wave number, a spectral resolution of 0.25 cm⁻¹, and a signal-to-noise ratio better than 50,000:1. Full article

Accurate fruit detection is of great importance for yield assessment, timely harvesting, and orchard management strategy optimization in precision agriculture. Traditional optical imaging methods are limited by lighting and meteorological conditions, making it difficult to obtain stable, high-quality data. Therefore, this study utilizes near-field millimeter-wave MIMO-SAR (Multiple Input Multiple Output Synthetic Aperture Radar) technology, which is capable of all-day and all-weather imaging, to perform high-precision detection of apple targets in orchards. This paper first constructs a near-field millimeter-wave MIMO-SAR imaging system and performs multi-angle imaging on real fruit tree samples, obtaining about 150 sets of SAR-optical paired data, covering approximately 2000 accurately annotated apple targets. Addressing challenges such as weak scattering, low texture contrast, and complex backgrounds in SAR images, we propose an innovative detection framework integrating Dynamic Spatial Pyramid Pooling (DSPP), Recursive Feature Fusion Network (RFN), and Context-Aware Feature Enhancement (CAFE) modules. DSPP employs a learnable adaptive mechanism to dynamically adjust multi-scale feature representations, enhancing sensitivity to apple targets of varying sizes and distributions; RFN uses a multi-round iterative feature fusion strategy to gradually refine semantic consistency and stability, improving the robustness of feature representation under weak texture and high noise scenarios; and the CAFE module, based on attention mechanisms, explicitly models global and local associations, fully utilizing the scene context in texture-poor SAR conditions to enhance the discriminability of apple targets. Experimental results show that the proposed method achieves significant improvements in average precision (AP), recall rate, and F1 score on the constructed near-field millimeter-wave SAR apple dataset compared to various classic and mainstream detectors. Ablation studies confirm the synergistic effect of DSPP, RFN, and CAFE. Qualitative analysis demonstrates that the detection framework proposed in this paper can still stably locate apple targets even under conditions of leaf occlusion, complex backgrounds, and weak scattering. This research provides a beneficial reference and technical basis for using SAR data in fruit detection and yield estimation in precision agriculture. Full article