Imaging Plate Research Articles

Efficient Vehicle Registration Recognition System: Enhancing Accuracy and Power Efficiency through Digital Image Processing

Number-plate recognition technology uses optical character recognition on images to read vehicle registration plates using OpenCV, PyTesseract OCR Engine, and YOLOv4 model. Images of the license plates are provided to the YOLOv4 model using a web app. The model is trained with the help of images from the Kaggle dataset. Then the number plate is extracted from the vehicle using the model. The string from the image is extracted using PyTesseract OCR Engine. Tested on several datasets this method gave us a success rate of 94%. This method can also be used in real-time incidents.

DiffPlate: A Diffusion Model for Super-Resolution of License Plate Images

License plate recognition is a pivotal challenge in surveillance applications, predominantly due to the low resolution and diminutive size of license plates, which impairs recognition accuracy. The advent of AI-based super-resolution techniques offers a promising avenue to ameliorate the resolution of such images. Despite the deployment of various super-resolution methodologies, including Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), the quest for satisfactory outcomes in license plate image enhancement persists. This paper introduces “DiffPlate”, a novel Diffusion Model specifically tailored for license plate super-resolution. Leveraging the unprecedented capabilities of Diffusion Models in image generation, DiffPlate is meticulously trained on a dataset comprising low-resolution and high-resolution pairs of Saudi license plates, curated for our surveillance application. Our empirical analysis substantiates that DiffPlate markedly eclipses state-of-the-art alternatives such as SwinIR and ESRGAN, evidencing a 26.47% and 37.32% enhancement in Peak Signal-to-Noise Ratio (PSNR) against these benchmarks, respectively. Furthermore, DiffPlate achieves superior performance in terms of Structural Similarity Index (SSIM), with a 4.88% and 16.21% improvement over SwinIR and ESRGAN, respectively. Human evaluative studies further corroborate that images refined by DiffPlate were preferred 92% more frequently compared to those processed by other algorithms. Through DiffPlate, we present a new solution to the license plate super-resolution challenge, demonstrating significant potential for adoption in real-world surveillance systems.

High repetition-rate dual-channel X-ray spectrometer for high-intensity laser-plasma experiments.

We describe the development and demonstration of a high-repetition-rate-capable dual-channel (DC) x-ray spectrometer designed for high-intensity laser-plasma experiments (≥1×1021 W/cm2). The spectrometer, which operates at high repetition rates, is limited only by the refresh rate of targets and the camera's frame rate. It features two channels, each equipped with a flat highly oriented pyrolytic graphite (HOPG) crystal and a unique detector plane, allowing it to resolve two distinct x-ray bands: approximately 7-10 and 10-13keV. Each detector plate carrier holds two slots for active (scintillators) or passive (imaging plates) x-ray detectors. We present the design and testing of the HR-DC-HOPG using both the COMET laser (10J, 0.5ps shot/4min) at LLNL's Jupiter Laser Facility and the SCARLET laser (10J, 30fs shot/min) at Ohio State University. The results demonstrate the spectrometer's performance across various laser energies, target materials, pulse shapes, and detector types.

A modular, high dynamic range passive neutron dosimeter and imaging diagnostic.

The multi-decade neutron dosimeter and imaging diagnostic (MDND) is a passive diagnostic that utilizes the polyethylene (n, p) nuclear reaction to enhance the diagnostic's sensitivity for time and energy integrated neutron measurements in the range of 2.45-14.1MeV. The MDND utilizes a combination of radiochromic film, phosphor image plates, and solid-state nuclear track detectors, with the goal of providing several orders of magnitude of dynamic range in terms of measured neutron fluence. The diagnostic design was guided by simulations in the Monte Carlo N-Particle (MCNP) transport code to determine the optimum thickness of the polyethylene convertor for maximum proton fluence incident on the detection medium as a function of incident neutron energy. In addition, the simulation results of complete diagnostic assemblies, or "stacks," were used to determine the total dynamic range of an MDND in terms of measured neutron source yield, which was found to be between around 107 and 1015 emitted into 4π with the detector located 1m away from the source. Complimentary to these simulations, individual detectors within a stack were simulated and analyzed to determine response as a function of neutron energy and yield. This work presents the diagnostic design, MCNP simulation results, and analysis of expected signals for varying neutron sources.

Absolute Method for Measuring Environmental Radioactive Materials Using Imaging Plates

Abstract We had previously developed a measurement method using an imaging plate (IP) to evaluate and address surface contamination caused by the release of radioactive materials during the Fukushima nuclear accident. The measurement units for the surface contamination density were in relative values [described as relative luminescence levels measured in luminescence arbitrary units (LAU)], but the evaluation was required in absolute values, such as Bq cm−2, to enable appropriate control of exposure doses. This study establishes a method for converting the IP measurements of surface contamination density due to environmental radioactivity into absolute values. Soil contaminated with radioactive materials from the Fukushima nuclear accident was collected to create a working reference material (WRM). The conversion coefficient for surface contamination density was calculated using the WRM values measured with an IP and high-purity germanium detectors. The IP measurement values were converted into the surface contamination density using the conversion coefficient. The WRM values measured with the IP and high-purity germanium detectors were 324.1 LAU and 32.22 ± 2.27 Bq cm−2, respectively. The surface contamination density conversion factor was calculated as 0.0994. The surface contamination density on the roof of the Tsukuba City facility was re-evaluated using the conversion factor. The average value of 29,972 Bq m−2 matched the amount of radioactive material fallen in Tsukuba City. By standardizing the measurement conditions for surface contamination when using IPs, we successfully quantified the surface contamination density with an accuracy comparable to that of conventional methods. This method is expected to make a significant contribution to efficient radiation safety management.

Tettigoniinae (Orthoptera: Tettigoniidae) from South-West of Turkey, with an incognito new species and comments on the genus Eupholidoptera

Twenty species are recorded from SW of Turkey. One new species, Eupholidoptera abscondita sp. nov., is described. An overlooked previous synonym of Eupholidoptera tucherti Harz, 1988 with E. krueperi (Ramme, 1930) is confirmed and Eupholidoptera helina Çıplak, 2009 is synonymized with E. smyrnensis (Brunner von Wattenwyl, 1882). Twenty-one Eupholidoptera species from Turkey and the Middle East are compared with images of the female subgenital plates.

Use of phase plate cryo-EM reveals conformation diversity of therapeutic IgG with 50 kDa Fab fragment resolved below 6 Å

While cryogenic electron microscopy (cryo-EM) is fruitfully used for harvesting high-resolution structures of sizable macromolecules, its application to small or flexible proteins composed of small domains like immunoglobulin (IgG) remain challenging. Here, we applied single particle cryo-EM to Rituximab, a therapeutic IgG mediating anti-tumor toxicity, to explore its solution conformations. We found Rituximab molecules exhibited aggregates in cryo-EM specimens contrary to its solution behavior, and utilized a non-ionic detergent to successfully disperse them as isolated particles amenable to single particle analysis. As the detergent adversely reduced the protein-to-solvent contrast, we employed phase plate contrast to mitigate the impaired protein visibility. Assisted by phase plate imaging, we obtained a canonical three-arm IgG structure with other structures displaying variable arm densities co-existing in solution, affirming high flexibility of arm-connecting linkers. Furthermore, we showed phase plate imaging enables reliable structure determination of Fab to sub-nanometer resolution from ab initio, yielding a characteristic two-lobe structure that could be unambiguously docked with crystal structure. Our findings revealed conformation diversity of IgG and demonstrated phase plate was viable for cryo-EM analysis of small proteins without symmetry. This work helps extend cryo-EM boundaries, providing a valuable imaging and structural analysis framework for macromolecules with similar challenging features.

Research on Detection Efficiency of Imaging Plates for Alpha Particles Using Two Types of Imaging Plate.

Imaging plates can measure isotopes with alpha decay (such as radon and its progeny, americium, and so on). However, the detection efficiency of imaging plates is affected by alpha particle energy, types of imaging plates, and the overlapping effect. In this study, simulations were performed to analyze the relationship between detection efficiency and these three influence factors. The research findings suggest that BAS-TR and BAS-MS are well-suited for the detection of alpha particles with energy levels below 6.83 MeV and above, respectively. The track overlap effect correction method proposed in this study is applicable to both BAS-TR and BAS-MS image plates. The measurement results of radon progeny demonstrate that the correction method enhances the detection efficiency from 0.203 to 0.288. This study presents a valuable approach for selecting the appropriate image plate and correcting the track overlap effect in the measurement of alpha radioactive material concentration and other related information.

An equivalent target plate damage probability calculation mathematics model and damage evaluation method

Aiming at the requirement of damage testing and evaluation of equivalent target plate based on the explosion of intelligent ammunition, this paper proposes a novel method for damage testing and evaluation method of circumferential equivalent target plate. Leveraging the dispersion characteristics parameters of fragment, we establish a calculation model of the fragment power situation and the damage calculation model under the condition of fragment ultimate penetration equivalent target plate. The damage model of equivalent target plate involves the fragment dispersion density, the local perforation damage criterion, the tearing damage model, and the damage probability. We use the camera to obtain the image of the equivalent target plate with fragment perforation, and research the algorithm of fragment distribution position recognition and fragment perforation area calculation method on the equivalent target plate by image processing technology. Based on the obtained parameters of the breakdown position and perforation area of fragments on equivalent target plate, we apply to damage calculation model of equivalent target plate, and calculate the damage probability of each equivalent target plate, and use the combined probabilistic damage calculation method to obtain the damage evaluation results of the circumferential equivalent target plate in an intelligent ammunition explosion experiment. Through an experimental testing, we verify the feasibility and rationality of the proposed damage evaluation method by comparison, the calculation results can reflect the actual damage effect of the equivalent target plate.

SterAF: A Scene Text Recognizer with Appearance-Flow Rectification

As the demand for recognizing irregular text in natural scenes increases, people are increasingly realizing the value of such applications, such as license plate recognition systems, image search, handwriting recognition, and autonomous driving, which are profoundly changing our lives in the field of text recognition. Recent studies have shown that the recognition of curved text and perspective text has become an important challenge in the field of text recognition, and the correction of curved text is a key step to achieve accurate recognition. However, current methods use strained text image correction methods, resulting in poor recognition accuracy when recognizing curved text. Therefore, we propose an end-to-end framework called Scene Text Recognizer with Appearance-Flow rectification (SterAF), which includes a correction network and a recognition network. Specifically, the framework’s steps are as follows: first, the input text image is deformed through an appearance flow-based correction network to adaptively warp the text image, to prevent irregular and unnatural deformations of the text image. Second, a sequence-to-sequence recognition network predicts the sequence of characters in the corrected text image to accurately recognize the text in the image. Through subjective and objective experiments, our SterAF model has shown excellent performance in both qualitative and quantitative experiments.

A Bio-Inspired Retinal Model as a Prefiltering Step Applied to Letter and Number Recognition on Chilean Vehicle License Plates

This paper presents a novel use of a bio-inspired retina model as a scene preprocessing stage for the recognition of letters and numbers on Chilean vehicle license plates. The goal is to improve the effectiveness and ease of pattern recognition. Inspired by the responses of mammalian retinas, this retinal model reproduces both the natural adjustment of contrast and the enhancement of object contours by parvocellular cells. Among other contributions, this paper provides an in-depth exploration of the architecture, advantages, and limitations of the model; investigates the tuning parameters of the model; and evaluates its performance when integrating a convolutional neural network and a spiking neural network into an optical character recognition (OCR) algorithm, using 40 different genuine license plate images as a case study and for testing. The results obtained demonstrate the reduction of error rates in character recognition based on convolutional neural networks (CNNs), spiking neural networks (SNNs), and OCR. It is concluded that this bio-inspired retina model offers a wide spectrum of potential applications to further explore, including motion detection, pattern recognition, and improvement of dynamic range in images, among others.

Computed chest radiography for total body irradiation: image quality and clinical feasibility

Objective. In myeloablative total body irradiation (TBI), lung shielding blocks are used to reduce the dose to the lungs and hence decrease the risk of radiation pneumonitis. Some centers are still using mega-Volt (MV) imaging with dedicated silver halide-based films during simulation and treatment for lung delineation and position verification. However, the availability of these films has recently become an issue. This study examines the clinical performance of a computed radiography (CR) solution in comparison to radiographic films and potential improvement of image quality by filtering and post-processing. Approach. We compared BaFBrI-based CR plates to radiographic films. First, images of an aluminum block were analyzed to assess filter impact on scatter reduction. Secondly, a dedicated image quality phantom was used to assess signal linearity, signal-to-noise ratio (SNR), contrast and spatial resolution. Ultimately, a clinical performance study involving two impartial observers was conducted on an anthropomorphic chest phantom, employing visual grading analysis (VGA). Various filter materials and positions as well as post-processing were examined, and the workflow between CR and film was compared. Main results. CR images exhibited high SNR and linearity but demonstrated lower spatial and contrast resolution when compared to film. However, filtering improved contrast resolution and SNR, while positioning filters inside the cassette additionally enhanced sharpness. Image processing improved VGA scores, while additional filtering also resulted in higher spine visibility scores. CR shortened TBI simulation by over 10 minutes for one patient, alongside a dose reduction by order of 0.1 Gy. Significance. This study highlights potential advantages of shifting from conventional radiographic film to CR for TBI. Overall, CR with the incorporation of processing and filtering proves to be suitable for TBI chest imaging. When compared to radiographic film, CR offers advantages such as reduced simulation time and dose delivery, re-usability of image plates and digital workflow integration.

Use of parametric X-ray radiation of electrons in crystals to determine the parameters of imaging-plates

Good agreement between experimental data and the results of calculations of the yield and angular distributions of parametric X-ray radiation of electrons in crystals within the framework of kinematic theory enables us to use the measurement results to determine the photon energy dependence of the sensitivity of X-ray plates. The results of measurements of the angular distributions of parametric X-ray radiation of electrons with an energy of 255 MeV in a Si crystal using imaging plates of several types were compared with the results of calculations that take into account all currently known experimental factors that influence the measurement results. The spectral dependence of the sensitivity of the studied X-ray plates was determined for photon energies of two orders of reflection and for the (111), (1̅1̅1), and (110) reflection planes.

Deep learning adversarial attacks and defenses on license plate recognition system

The breakthroughs in Machine learning and deep neural networks have revolutionized the handling of critical practical challenges, achieving state-of-the-art performance in various computer vision tasks. Notably, the application of deep neural networks in optical character recognition (OCR) has significantly enhanced the performance of OCR systems, making them a pivotal preprocessing component in text analysis pipelines for crucial applications such as license plate recognition (LPR) systems, where the efficiency of OCR is paramount. However, despite the advancements, the integration of deep neural networks in OCR introduces inherent security vulnerabilities, particularly susceptibility to adversarial examples. Adversarial examples in LPR systems are crafted by introducing perturbations to original license plate images, which can effectively compromise the integrity of the license plate recognition process, leading to erroneous license plate number identification. Given that the primary goal of OCR in this context is to accurately recognize license plate numbers, even a single misinterpreted character can significantly impact the overall performance of the LPR system. The vulnerability of LPR systems to adversarial attacks underscores the urgent need to address the security weaknesses inherited from deep neural networks. In response to these challenges, the exploration of alternative defense mechanisms, such as image denoising and in-painting, presents a compelling approach to bolstering the resilience of LPR systems against adversarial attacks. By prioritizing practical implementation and integration of image denoising and inpainting techniques align with the operational requirements of real-world LPR systems. These methods can be seamlessly integrated into existing pipelines, offering pragmatic and accessible means of enhancing security without imposing significant computational overhead. By embracing a multi-faceted approach that combines the strengths of traditional image processing techniques, the research endeavors to develop comprehensive and versatile defense strategies tailored to the specific vulnerabilities and requirements of LPR systems. This holistic approach aims to fortify LPR systems against adversarial threats, thereby fostering increased trust and reliability in the deployment of OCR and LPR technologies across various domains and applications.

Enhancing Bangladeshi License Plate Recognition: A YOLOv8 Approach with Roboflow Integration for Accuracy and Speed Optimization

Abstract: Automatic License Plate Recognition (ALPR) systems are indispensable in modern transportation services, offering crucial benefits in traffic management, parking, toll collection, and surveillance. However, implementing ALPR in Bangladesh presents challenges due to the intricacies of Bangla characters and low-resolution CCTV images. Despite ALPR's potential to enhance safety and security, issues such as license plate variations and image quality hinder its effective implementation. This research addresses these challenges by developing a customized YOLOv8 model tailored for recognizing Bengali license plates, with a focus on precise localization, character segmentation, and script deciphering. Leveraging advanced deep learning techniques, the study aims to enhance efficiency and accuracy for applications in law enforcement and traffic management. Through a comprehensive workflow integrating Roboflow and YOLOv8, effective dataset collection, annotation, augmentation, and model training are demonstrated. System evaluation on diverse test datasets confirms the reliable detection and recognition of Bangladeshi license plates, underscoring the model's practical utility and robustness in real-world scenarios. Additionally, the research showcases notable advancements in ALPR technology specific to Bangladesh's unique context. Noteworthy is the model's exceptional performance in detecting license plates from corner angles, even across various vehicle types, as well as its ability to accurately identify plates amid challenging conditions such as broken or obscured plates and low-resolution images. Moreover, the system's proficiency in scenarios with multiple license plates, like those on buses adorned with banners, contributes significantly to improved road safety and traffic management.

COST ASSESSMENT OF COMPUTED RADIOGRAPHY TOWARDS CONVENTIONAL RADIOGRAPHY

Introduction: With the expansion of the development of computer equipment and the improvement of software interfaces, there is also a sudden development of radiology without film, through computerized radiography (CR) systems. The CR system made it possible to improve the quality of the radiological image, and thus to make a more accurate and faster diagnosis.Objective: To estimate the costs of computed radiography compared to conventional radiography.Materials and methods: The analysis used data on the costs of radiological materials and existing radiological information systems from the plans and signed contracts for 2019 and 2022 in the Croatian Hospital "Dr. Fr. Mato Nikolić" Nova Bila.Results: Thecosts of conventional radiology are exceptionally high, including the procurement of X-ray films, processing chemicals, and equipment maintenance. The analysis compares the number of procedures and the displayed costs for the hospital.Conclusion: Computerized radiography is more cost-effective than conventional radiography due to reusable phosphor imaging plates and reduced chemical and film expenses.Keywords: Diagnostic Imaging, Radiography, Cost-Effectiveness Analysis,Radiology Information Systems

The Effect of 147 MeV 84Kr and 24.5 MeV 14N Ions Irradiation on the Optical Absorption, Luminescence, Raman Spectra and Surface of BaFBr Crystals

Today, BaFBr crystals activated by europium ions are used as detectors that store absorbed energy in metastable centers. In these materials, the image created by X-ray irradiation remains stable in the dark for long periods at room temperature. As a result, memory image plates are created, and they are extended to other types of ionizing radiation as well. Despite significant progress towards X-ray storage and readout of information, the mechanisms of these processes have not been fully identified to date, which has hindered the efficiency of this class of phosphors. In this study, using photoluminescence (PL), optical absorption (OA), Raman spectroscopy (RS), and atomic force microscopy (AFM), the luminescence of oxygen vacancy defects to BaFBr crystals irradiated with 147 MeV 84Kr and 24.5 MeV 14N ions at 300 K to fluences (1010–1014) ion/cm2 was investigated. BaFBr crystals were grown by the Shteber method on a special device. Energy-dispersive X-ray spectroscopy (EDX) analysis revealed the presence of Ba, Br, F, and O. The effect of oxygen impurities present in the studied crystals was considered. The analysis of the complex PL band, depending on the fluence and type of ions, showed the formation of three types of oxygen vacancy defects. Macrodefects (tracks) and aggregates significantly influence the luminescence of oxygen vacancy defects. The creation of hillocks and tracks in BaFBr crystals irradiated with 147 MeV 84Kr ions is shown for the first time. Raman spectra analysis confirmed that BaFBr crystals were amorphized by 147 MeV 84Kr ions due to track overlap, in contrast to samples irradiated with 24.5 MeV 14N ions. Raman and absorption spectra demonstrated the formation of hole and electron aggregate centers upon swift heavy ions irradiation.

Improving license plate recognition via diverse stylistic plate generation

Deep learning-based license plate recognition heavily relies on the quantity and diversity of training images. However, manually collecting diverse license plate images is a highly time-consuming and labor-intensive task. Hence, generating license plate images to supplement training data emerges as a cost-effective solution for recognition enhancement. Previous generation methods treated the style of license plates as discrete features and extracted style features from a referenced image to generate multi-style license plate images. However, these methods are incapable of generating license plates with arbitrary new styles, as the style of the generated images is constrained by the existing images. To solve this problem, we propose to extract style features from randomly sampled vectors in continuous space for diverse stylistic license plate generation. This way, license plates with arbitrary styles can be generated by simply modifying the sampled style vectors. Besides, to provide controllable content information for the generator, we propose an orientation-aware content encoder to extract multi-oriented glyph content features from the input horizontal glyphs. Moreover, a dual-discriminator is proposed to conduct adversarial learning on both color images and image gradients, aiming to mitigate unnatural patterns and ensure realistic generated results. Finally, extensive experiments on multiple public license plate datasets demonstrate that our method can generate realistic and diverse license plates, and the generated images can significantly improve recognition performance by up to 11.5%.

Measurement of 2p-3d absorption in a hot molybdenum plasma

We present measurements of the 2p-3d transition opacity of a hot molybdenum–scandium sample with nearly half-vacant molybdenum M-shell configurations. A plastic-tamped molybdenum–scandium foil sample is radiatively heated to high temperature in a compact D-shaped gold Hohlraum driven by ∼30 kJ laser energy at the SG-100 kJ laser facility. X rays transmitted through the molybdenum and scandium plasmas are diffracted by crystals and finally recorded by image plates. The electron temperatures in the sample in particular spatial and temporal zones are determined by the K-shell absorption of the scandium plasma. A combination of the IRAD3D view factor code and the MULTI hydrodynamic code is used to simulate the spatial distribution and temporal behavior of the sample temperature and density. The inferred temperature in the molybdenum plasma reaches a average of 138 ± 11 eV. A detailed configuration-accounting calculation of the n = 2–3 transition absorption of the molybdenum plasma is compared with experimental measurements and quite good agreement is found. The present measurements provide an opportunity to test opacity models for complicated M-shell configurations.

Evaluation of the effects of postprocessing settings in digital bitewing radiographs on proximal caries detection.

Radiographs are an integral part of detecting proximal caries. The aim of this study was to evaluate the effect of contrast, brightness, noise, sharpness, and γ adjustment of digital intraoral radiographs on the diagnosis of proximal caries. In this in vitro study, 40 extracted teeth including 20 premolars and 20 molars with enamel lesions (white spot or dentin discoloration seen through the enamel) were mounted together in groups of eight inside the skull. Bitewing radiographic images of each dental group were obtained by a photostimulable phosphor plate sensor with exposure conditions of 8 mA, 70 kV, and 0.2 s. The images were reconstructed by the built-in software and examined by two oral and maxillofacial radiologists in various settings of contrast, brightness, sharpness, noise, and γ. The teeth were then cut mesiodistally and the presence or absence of caries was confirmed by an oral and maxillofacial pathologist using a stereomicroscope. The data were then analyzed using the κ agreement coefficient, sensitivity, specificity, and accuracy (α = .05). Adjustment of brightness and contrast led to higher diagnostic performance with an accuracy of 82.5% and 83.8 (for observers 1 and 2, respectively) and 82.5% (for both observers), respectively. Noise adjustment was the least helpful approach for diagnosis of proximal dental caries among other adjustments, with an accuracy of 78.8% and 77.5% for observers 1 and 2, respectively. Brightness and contrast setting was more efficient in improving the diagnostic potential of bitewing radiographs compared to other adjustments.