Journal of Radiation Research and Applied Sciences最新文献_第4页

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model 使用 YOLO NAS 深度学习模型对脑肿瘤 MRI 图像进行检测和分类

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-10-08 DOI: 10.1016/j.jrras.2024.101113

M.S. Mithun , S. Joseph Jawhar

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

如果脑肿瘤得不到正确诊断，可能会导致致命后果和重大健康问题。因此，诊断的一个关键环节是早期识别脑肿瘤，并对脑肿瘤类型进行精确分类。这项研究工作的重点是利用 YOLO NAS 模型从脑肿瘤 MRI 图像中检测和分类垂体瘤、脑膜瘤、胶质瘤和非肿瘤。深度学习的进步使人们对计算机辅助诊断技术的认识不断提高。为了提高分类性能，输入数据集是利用脑肿瘤磁共振图像数字数据库从 REMBRANDT 数据库中获取的。这项任务的主要阶段包括分割、分类和预处理。研究人员对 RGB 图像进行预处理，以排除任何不需要的斑点，然后再定位 ROI。对于每张脑肿瘤图像，我们都使用了混合各向异性扩散滤波（HADF）技术来去除噪声。编码器-解码器网络（En-DeNet）使用基于 U-Net 的深度神经网络作为编码器，并使用预先训练好的 EfficientNet 作为解码器，然后用于分割 MRI 图像。利用基于深度学习的 YOLO NAS 技术开发了一个拟议模型，用于从磁共振成像图像中识别脑膜瘤肿瘤、非肿瘤、胶质瘤肿瘤和垂体瘤等脑癌。建议模型的分类性能根据精确度、F1-分数、灵敏度、准确度和特异性等参数进行权衡。使用 2570 个 MRI 数据进行脑肿瘤病例的训练，并使用 630 个数据进行测试和验证，所提出的融合方法 YOLO NAS 在准确性（AC = 0.997%）、特异性（SP = 0.985%）、精确性（PR = 0.982%）、F1 分数（F1 = 0.992%）和灵敏度（SE = 0.985%）方面的分类结果均有所改善。结果表明，基于 YOLO NAS 技术的脑肿瘤类型分类系统优于 DNN、PDCNN、DenseNet-161 和 DCNN-SGD 模型分类器。

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引用次数: 0

Thin film flows on a linearly moving surface with thermocapillary effects and variable heat generation/absorption 线性运动表面上的薄膜流，具有热毛细管效应和可变的发热/吸热现象

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-10-07 DOI: 10.1016/j.jrras.2024.101135

Pradeep G. Janthe , Jagadish V. Tawade , Haitham A. Mahmoud , Mohammed El-Meligy , M. Ijaz Khan

Current research focuses on thin liquid films on a linear stretching sheet, taking into account the effects of thermocapillary, magnetic fields, thermal radiation, viscous dissipation, and variable heat generation/absorption in a porous media. Similarity transformations are used to turn the governing equations into a set of linked ordinary differential equations (ODE). The coupled ODEs regulating motion, heat, and mass are numerically solved. The experiment's results reveal that different factors affect mass transfer, temperature, and velocity differently. These effects are addressed by a detailed analysis and graphical representations, which indicate that the flow temperature decreases consistently with increasing thermocapillary number, By understanding thermocapillary flow interactions, industries can enhance coating efficiency, uniformity, and thermal management, whereas the velocity profile initially slows, then increases. These data shed important light on the intricate dynamics including mass transfer, temperature, and velocity.

目前的研究重点是线性拉伸薄片上的液体薄膜，同时考虑多孔介质中热毛细管、磁场、热辐射、粘性耗散和可变发热/吸热的影响。利用相似变换将支配方程转化为一组相关联的常微分方程（ODE）。对调节运动、热量和质量的耦合 ODE 进行数值求解。实验结果表明，不同因素对传质、温度和速度的影响各不相同。通过了解热毛细管流的相互作用，工业界可以提高涂层效率、均匀性和热管理。这些数据揭示了包括传质、温度和速度在内的复杂动态。

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引用次数: 0

Advanced deep learning strategies for breast cancer image analysis 用于乳腺癌图像分析的高级深度学习策略

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-10-05 DOI: 10.1016/j.jrras.2024.101136

Houmem Slimi, Sabeur Abid, Mounir Sayadi

One of the leading causes of cancer-related deaths in women is breast cancer (BC). Patients' chances of survival are improved when this ailment is diagnosed in a timely manner and suitable treatments are prescribed. Thus, the chance of survival increases with the early detection of BC. Deep learning neural networks have garnered significant attention in recent years for use in BC screening, identification, and classification. Even though some encouraging results have surfaced, more improvement and confirmation are necessary. In this regard, the creation and comparison of many deep learning approaches for the early identification and classification of BC from mammography pictures is the main focus of our research. In this study, Innovative deep learning methods are developed by freezing the first 40 layers an dropping the 43 layers of MobileNetV2, InceptionV3 and DenseNet121 pretrained models, this approach was reinforced by using Data augmentation techniques. Our suggested methodologies are tested through simulations on MIAS dataset and SA private dataset. The three modified models achieved very high classification accuracies, specifically the modified DenseNet121 model, which reached 99,1% on MIAS dataset and 98,8% on SA dataset.

乳腺癌（BC）是女性死于癌症的主要原因之一。如果能及时诊断出这种疾病并采取适当的治疗方法，患者的生存几率就会提高。因此，乳腺癌的早期发现会增加患者的生存机会。近年来，深度学习神经网络在BC筛查、识别和分类中的应用备受关注。尽管已经出现了一些令人鼓舞的结果，但还需要更多的改进和证实。在这方面，我们的研究重点是创建和比较多种深度学习方法，用于从乳腺 X 射线照片中早期识别和分类乳腺癌。在这项研究中，我们通过冻结 MobileNetV2、InceptionV3 和 DenseNet121 预训练模型的前 40 层并放弃 43 层，开发了创新的深度学习方法，并通过使用数据增强技术强化了这种方法。我们建议的方法在 MIAS 数据集和 SA 私人数据集上进行了模拟测试。三个修改后的模型达到了非常高的分类准确率，特别是修改后的 DenseNet121 模型，在 MIAS 数据集上达到了 99.1%，在 SA 数据集上达到了 98.8%。

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引用次数: 0

Features of microorganism and two-phase nanofluid in a tangent hyperbolic Darcy-Forchhiemer flow induced by a stretching sheet with Lorentz forces 微生物和两相纳米流体在具有洛伦兹力的拉伸片材诱导的切线双曲达西-福尔希默流中的特征

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-10-03 DOI: 10.1016/j.jrras.2024.101130

M. Faizan Ahmed , Farhan Ali , Syed Sohaib Zafar , Umair Khan , Yalcin Yilmaz , Nermeen Abdullah , Samia Elattar , Aurang Zaib , Ahmed M. Galal

This work examines the two-dimensional tangent hyperbolic flow over a stretching sheet with a uniform magnetic field. The Buongiorno model is utilized to analyze and explain the spread of uneven coefficients in the presence of gyrotactic microorganisms. The concept of microorganisms and the resulting bioconvection enhance the stability of the nanoparticles. The impacts of thermal radiation, heat sources, convective heating, and chemical reactions are also evaluated. The suggested mathematical problem results in a nonlinear set of partial differential equations (PDEs), which are subsequently reduced to ordinary differential equations (ODEs) by applying the appropriate transformation. The resultant highly nonlinear ordinary differential equations (ODEs) are numerically solved using MATLAB's built-in package known as bvp4c. An in-depth investigation into the changes in the velocity field, the temperature profile, the concentration of nanoparticles profile, and the motile density profile is analyzed through graphs against various influencing parameters. Additionally, computations of engineering interest quantities such as skin friction, local Nusselt number, local Sherwood number, and molecular density are presented in both graphical and tabular formats for further examination. It has been explored that with greater values of the Weissenberg number, the fluid velocity upsurges when

n < 1

, whereas the opposite behavior is noticed when

n > 1

. It is also noted that an increment in the Peclet number decreases motile density for both dilatants

(n > 1)

and pseudoplastic

(n < 1)

fluids. The computed results are compared with existing literature in limiting cases and found good agreement.

这项研究探讨了均匀磁场下拉伸片上的二维切线双曲流动。利用 Buongiorno 模型分析和解释了陀螺接触微生物存在时不均匀系数的扩散。微生物的概念和由此产生的生物对流增强了纳米粒子的稳定性。此外，还评估了热辐射、热源、对流加热和化学反应的影响。所提出的数学问题产生了一组非线性偏微分方程 (PDE)，随后通过应用适当的变换将其简化为常微分方程 (ODE)。由此产生的高度非线性常微分方程（ODEs）使用 MATLAB 内置的 bvp4c 软件包进行数值求解。通过与各种影响参数相对应的图表，对速度场、温度曲线、纳米颗粒浓度曲线和运动密度曲线的变化进行了深入研究分析。此外，还以图形和表格的形式展示了工程相关量的计算结果，如表皮摩擦、局部努塞尔特数、局部舍伍德数和分子密度，以供进一步研究。研究还发现，随着韦森伯格数值的增大，当 n<1 时流体速度会上升，而当 n>1 时则相反。计算结果与现有文献中的极限情况进行了比较，发现两者吻合得很好。

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引用次数: 0

The genotoxicity effects and oxidative stress of common volatile and injectable anesthesia drugs on peripheral blood during irradiation of BALB/c mice 照射 BALB/c 小鼠时常见挥发性和注射性麻醉药物对外周血的遗传毒性效应和氧化应激作用

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-09-27 DOI: 10.1016/j.jrras.2024.101127

Chongyu Yang , Althaf Hussain Shaik , Turki Mayudh Alrubie , Yanxi Zhang

Introduction

Ionizing radiation (IR) is well-known for its genotoxic and cytotoxic effects. Additionally, anesthesia has been shown to cause various side effects, including genotoxicity. This study aims to evaluate the DNA damage and oxidative stress resulting from exposure to both anesthesia and IR.

Methods

Seventy BALB/c male mice were divided into 14 identical groups and anesthetized using three different inhalation anesthetics (isoflurane, sevoflurane, and halothane) and three different injectable anesthetics (propofol, ketamine, and thiopental). We also evaluated combinations of these anesthetic drugs with 1 Gy IR. DNA damage in white blood cells was assessed using the alkaline comet assay at 0, 2, and 24 h after treatment. Additionally, oxidative stress in blood samples was measured 6 h post-treatment.

Results

The percentage of DNA in the tail and fragmented nuclei after 2 and 24 h was significantly higher in the groups given volatile anesthetics compared to the propofol and thiopental groups (P<0.05). Furthermore, oxidative stress levels were also significantly higher in the volatile anesthetic groups (P<0.05). When combined with IR, the volatile anesthetics and ketamine resulted in increased genotoxicity and significantly higher oxidative stress compared to the group exposed only to IR (P<0.05). In contrast, propofol and thiopental did not lead to a significant increase in genotoxicity or oxidative stress compared to the control group (P>0.07).

Conclusion

Combining volatile anesthetic drugs and ketamine with 1 Gy irradiation resulted in higher levels of toxic effects and oxidative stress, although this combination did not produce a synergistic effect. Additionally, the combination of propofol and thiopental with IR did not show a significant difference compared to the irradiated-only group in both the alkaline comet and oxidative stress assays.

导言电离辐射（IR）以其基因毒性和细胞毒性作用而闻名。此外，麻醉也被证明会导致各种副作用，包括基因毒性。方法将 70 只 BALB/c 雄性小鼠分成 14 个相同的组，使用三种不同的吸入麻醉剂（异氟烷、七氟烷和氟烷）和三种不同的注射麻醉剂（异丙酚、氯胺酮和硫喷妥类）进行麻醉。我们还对这些麻醉药物与 1 Gy IR 的组合进行了评估。在治疗后 0、2 和 24 小时，我们使用碱性彗星试验评估了白细胞的 DNA 损伤情况。结果与异丙酚组和硫喷妥组相比，挥发性麻醉剂组在 2 和 24 小时后尾部 DNA 的百分比和细胞核碎片的百分比明显更高（P<0.05）。此外，挥发性麻醉剂组的氧化应激水平也明显更高（P<0.05）。与仅暴露于红外线的组别相比，挥发性麻醉剂和氯胺酮与红外线结合使用时，会导致基因毒性增加，氧化应激水平明显升高（P<0.05）。结论将挥发性麻醉药物和氯胺酮与 1 Gy 辐照结合使用会导致更高水平的毒性效应和氧化应激，尽管这种结合不会产生协同效应。此外，在碱性彗星试验和氧化应激试验中，异丙酚和硫喷妥同红外线照射组与单纯照射组相比没有显著差异。

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引用次数: 0

Thermo-physical properties of non-linear radiative flow of magnetized Ellis fluid comprising analysis of oxytactic microorganisms and nano-enhanced phase materials 磁化埃利斯流体非线性辐射流的热物理特性，包括氧接触微生物和纳米增强相材料分析

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-09-26 DOI: 10.1016/j.jrras.2024.101128

Waqar Azeem Khan , Nazash Anjum , Iftikhar Hussain , Mehboob Ali , S. Saleem , Rubina Sultana Mohammed

The basic aim of existing analysis is to scrutinize the two-dimensional flow regime of Ellis model under the consideration of magnetic field. The effect of thermophoresis as well as Brownian motion for Ellis model with the impacts of parameters like thermal radiation, bioconvection of microorganisms and viscous dissipation are also deliberated. On the other hand, the features of bioconvection have been systematically examined for its extensive uses in microbial developed oil recovery, gas-bearing alluvial basin, bio-microsystems and oil modeling. Under the effects of tension force, viscoelastic materials change according to viscoelastic and elastic illustration. Here we considered it due to hydro-dynamics unevenness and designs within suspension of subjective spinning microbes. Therefore, this paper presents a theoretical and computational evaluation of the unsteady 2D Bioconvection flow of Ellis fluid, which has non-linear radiation features as well as gyro-tactic microorganisms, illuminating the novelty of the work. Here, convective B. Cs are used for the current study, coupled PDEs are changed into coupled ODEs by using some suitable B. Cs and similarity conditions. The acquired ODEs are numerically solved by using bvp4c scheme. Here Physical effects concerning all such parameters are studied in detail and represented graphically. We found that increasing behavior of temperature for higher value of Brownian movement and thermophoresis parameter whereas fall in Prandtl number. The gyrotactic microbes result in reduction for rising values of Peclet number.

现有分析的基本目的是仔细研究 Ellis 模型在磁场作用下的二维流动机制。此外，还讨论了热辐射、微生物生物对流和粘性耗散等参数对 Ellis 模型的热泳效应和布朗运动的影响。另一方面，对生物对流的特征进行了系统研究，以了解其在微生物开发采油、含气冲积盆地、生物微系统和石油建模中的广泛应用。在拉力作用下，粘弹性材料会根据粘弹性和弹性图解发生变化。在这里，我们认为这是由于主观旋转微生物悬浮液中的流体力学不均匀性和设计造成的。因此，本文对埃利斯流体的非稳态二维生物对流进行了理论和计算评估，该流体具有非线性辐射特征以及陀螺触觉微生物，阐明了这项工作的新颖性。本研究使用对流 B. Cs，通过使用一些合适的 B. Cs 和相似性条件将耦合 PDE 变为耦合 ODE。获得的 ODEs 采用 bvp4c 方案进行数值求解。在这里，我们详细研究了所有这些参数的物理效应，并用图形表示出来。我们发现，布朗运动和热泳参数值越高，温度越高，而普朗特数则下降。回转运动微生物导致佩克莱特数值升高时温度降低。

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引用次数: 0

Reliability inference of a multicomponent stress-strength model for exponentiated Pareto distribution based on progressive first failure censored samples 基于渐进式首次失效普查样本的指数化帕累托分布多成分应力强度模型的可靠性推断

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-09-26 DOI: 10.1016/j.jrras.2024.101122

Najwan Alsadat , Amal S. Hassan , Mohammed Elgarhy , Mustapha Muhammad , Ehab M. Almetwally

Multicomponent stress-strength (MC-SS) analysis is crucial for risk management and decision-making in various fields such as engineering, manufacturing, and quality control. It helps in identifying vulnerable components and areas where improvements can enhance overall system reliability. A primary contribution of this research is the implementation of the progressive first-Failure censored (PFIF-C) scheme. This scheme offers a novel and efficient approach to time and cost censoring, surpassing many existing censoring schemes found in the literature. The current study investigates the issue of MC-SS reliability inference under PFIF-C from the exponentiated Pareto distribution. The reliability of the MC-SS system is considered under the condition that both stress and strength follow an exponentiated Pareto distribution with a common second shape parameter. The parameter estimates and reliability estimate of the MC-SS system is produced using the maximum likelihood and Bayesian procedures. The asymptotic confidence intervals and highest posterior density credible intervals for the MC-SS system reliability are produced. Bayesian estimates are yielded using Markov Chain Monte Carlo under both informative and non-informative priors, considering symmetric and asymmetric loss functions. In order to assess the efficacy of the suggested methodology, simulation analyses are conducted. According to the simulation data Bayesian estimates of the MC-SS reliability, employing both symmetric and asymmetric loss functions, consistently outperform maximum likelihood estimates in terms of estimated risks. In general, Bayesian estimates based on asymmetric loss function perform better than the other competing loss function. The procedure is further shown with one real-world data example about failure times of a specific software model to show how the recommended approach may be applied to assess the strength and stress of a multicomponent model.

多组件应力-强度（MC-SS）分析对于工程、制造和质量控制等各个领域的风险管理和决策至关重要。它有助于识别易受影响的组件以及可提高整体系统可靠性的改进领域。本研究的一个主要贡献是实施了渐进式首次失效删减（PFIF-C）方案。该方案提供了一种新颖、高效的时间和成本剔除方法，超越了文献中的许多现有剔除方案。本研究探讨了在 PFIF-C 方案下从指数化帕累托分布推断 MC-SS 可靠性的问题。MC-SS 系统的可靠性是在应力和强度都遵循具有共同第二形状参数的指数化帕累托分布的条件下考虑的。采用最大似然法和贝叶斯法计算 MC-SS 系统的参数估计和可靠性估计。得出了 MC-SS 系统可靠性的渐近置信区间和最高后验密度可信区间。在有信息和无信息先验条件下，考虑对称和非对称损失函数，使用马尔可夫链蒙特卡罗得出贝叶斯估计值。为了评估所建议方法的有效性，进行了模拟分析。根据模拟数据，采用对称和非对称损失函数的 MC-SS 可靠性贝叶斯估计值在估计风险方面始终优于最大似然估计值。一般来说，基于非对称损失函数的贝叶斯估算结果优于其他竞争性损失函数。该程序通过一个关于特定软件模型失效时间的实际数据示例进一步说明了如何将推荐方法应用于评估多组件模型的强度和应力。

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引用次数: 0

Unit extended exponential distribution with applications 单元扩展指数分布及其应用

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-09-26 DOI: 10.1016/j.jrras.2024.101118

Ibrahim E. Ragab , Najwan Alsadat , Oluwafemi Samson Balogun , Mohammed Elgarhy

In this paper, the unit extended exponential distribution is presented. The extended exponential distribution is a two-parameter lifespan distribution that has been demonstrated to be very adaptable. Our goal is to convert this flexibility between the unit interval and the extended exponential distribution. Several different types of probability density functions, including decreasing, left-skewed, right-skewed, and unimodal, have been shown. However, the hazard rate function may take on various forms, including J-shaped, U-shaped, and bathtub shapes. A few statistical properties, such as moments, mean, variance, skewness, kurtosis, expectation-weighted moments, incomplete moments, conditional moments, mean deviation, Lorenz and Bonferroni curves, mean residual life, mean inactivity time, and various entropy measures, round out the theoretical section. The maximum likelihood technique is utilized to estimate model parameters using unit data. This study provides simulation data to evaluate the proposed strategy. Two applications utilizing actual data sets emphasize the significance of the new model compared to existing unit models.

本文介绍了单位扩展指数分布。扩展指数分布是一种双参数寿命分布，已被证明具有很强的适应性。我们的目标是在单位区间和扩展指数分布之间转换这种灵活性。我们已经展示了几种不同类型的概率密度函数，包括递减型、左斜型、右斜型和单峰型。然而，危险率函数也有多种形式，包括 J 形、U 形和浴缸形。一些统计特性，如矩、均值、方差、偏斜度、峰度、期望加权矩、不完全矩、条件矩、平均偏差、洛伦兹曲线和邦费罗尼曲线、平均残余寿命、平均非活动时间以及各种熵指标，使理论部分更加完善。最大似然法是利用单位数据估算模型参数的方法。本研究提供了模拟数据来评估所提出的策略。利用实际数据集的两个应用强调了新模型与现有单元模型相比的重要性。

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引用次数: 0

Integrating gamma camera image and surveillance videos to track the pedestrian with radiation source 整合伽马相机图像和监控视频，追踪带有辐射源的行人

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-09-25 DOI: 10.1016/j.jrras.2024.101105

YiCong Zhou, XueTao Liu, YuFeng Xiao

In response to the demand for customs detection and tracking of individuals carrying radioactive sources, this paper proposes a method that integrates radiographic imaging information for identifying and tracking carriers. Initially, a portable source personnel visual tracking system equipped with a gamma camera is constructed to capture the position of radioactive sources and images of moving personnel within the security inspection area. Subsequently, a method is developed to link personnel targets identified by both the gamma camera and surveillance cameras, using Euclidean distance and intersecting areas to pinpoint carriers. Furthermore, leveraging the improved RepVGG reparameterization structure and residual networks, we accelerate Re-Identification (RE-ID) inference and propose a Deep Feature Tracking (DFT) method. This method tracks personnel by comparing feature similarities in adjacent frames. Experimental results validate the effectiveness of this method in identifying suspicious individuals carrying radioactive sources and in mapping the walking trajectories of these carriers using pre-deployed surveillance cameras.

为了满足海关检测和追踪携带放射源人员的需求，本文提出了一种整合放射成像信息的方法，用于识别和追踪携带者。首先，构建了一个配备伽马相机的便携式放射源人员视觉跟踪系统，用于捕捉放射源的位置和安检区域内移动人员的图像。随后，利用欧氏距离和相交区域，开发出一种方法，将伽马相机和监控摄像机识别出的人员目标联系起来，以精确定位放射源携带者。此外，利用改进的 RepVGG 重参数化结构和残差网络，我们加快了重新识别（RE-ID）推理的速度，并提出了一种深度特征跟踪（DFT）方法。这种方法通过比较相邻帧中的特征相似性来跟踪人员。实验结果验证了该方法在识别携带放射源的可疑人员以及利用预先部署的监控摄像机绘制这些携带者的行走轨迹方面的有效性。

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引用次数: 0

Dehazing algorithm for complex environment video images considering visual communication effects 考虑视觉传播效果的复杂环境视频图像去粒算法

IF 1.7 4区综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES

Journal of Radiation Research and Applied Sciences

Pub Date : 2024-09-25 DOI: 10.1016/j.jrras.2024.101093

Yisa Yu , Jianwen Li

The communication effect of outdoor computer vision systems is closely related to the dehazing performance of video images. Currently, video image restoration methods in complex environments are still in an urgent development stage. By analyzing the characteristics of smoke, considering the local and global consistency of clean videos, as well as the sparsity of smoke videos, and elaborating on low rank tensor recovery, a smoke removal model is constructed in complex environments. The alternating direction multiplier optimization algorithm is used to solve the dehazing model. The dehazing algorithm for video images in complex environments had ideal dehazing effects in different types of images. However, the dehazing effect of other dehazing algorithms was not particularly significant. The maximum iteration times for different video image dehazing algorithms were 30, 40, 65, and 80, respectively. The maximum resolution of the algorithm also showed a gradually decreasing trend with the increase of SNR. For the dehazing algorithm of video images in complex environments, the optimal region scales corresponding to the three target scales were 3.2, 3.3, and 4.2, and the accuracy obtained was 90.5%, 90.6%, and 90.8%, respectively. The normal range of SSIM for video image dehazing algorithm in complex environments was [-1,1]. The normal value of PSNR was 30–40 dB. The PSNR and SSIM of the four types of video images meet the requirements. The constructed complex environment video image dehazing algorithm provides valuable suggestions for the development and improvement of computer vision systems. The constructed complex environment video image dehazing algorithm greatly improves the dehazing performance of video images, thereby greatly enhancing the communication effect of outdoor computer vision systems.

户外计算机视觉系统的通信效果与视频图像的去噪性能密切相关。目前，复杂环境下的视频图像复原方法仍处于亟待开发的阶段。通过分析烟雾的特征，考虑干净视频的局部和全局一致性以及烟雾视频的稀疏性，并对低秩张量恢复进行阐述，构建了复杂环境下的烟雾去除模型。该模型采用交替方向乘法优化算法求解。复杂环境下视频图像的除杂算法在不同类型的图像中都有理想的除杂效果。然而，其他去毛刺算法的去毛刺效果并不特别明显。不同视频图像去毛刺算法的最大迭代时间分别为 30、40、65 和 80。随着信噪比的增加，算法的最大分辨率也呈逐渐下降的趋势。对于复杂环境下的视频图像去毛刺算法，三个目标尺度对应的最佳区域尺度分别为 3.2、3.3 和 4.2，获得的精度分别为 90.5%、90.6% 和 90.8%。复杂环境下视频图像去噪算法的 SSIM 正常范围为[-1,1]。PSNR 的正常值为 30-40 dB。四类视频图像的 PSNR 和 SSIM 均符合要求。所构建的复杂环境视频图像去毛刺算法为计算机视觉系统的开发和改进提供了有价值的建议。所构建的复杂环境视频图像去毛刺算法极大地提高了视频图像的去毛刺性能，从而大大增强了室外计算机视觉系统的通信效果。

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