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real-time traffic data: estimating noise and air pollution, comparative ML techniques analysis

PROCEEDINGS OF THE INSTITUTION OF CIVIL ENGINEERS-TRANSPORT 2025年

作者： Madhu, Kavitha Athira, P. R. Rohini, S. Sikhin, V. C. Balakrishnan, Srijith TKM Coll Engn Dept Civil Engn Kollam India Rockwell Automat Co Bengaluru Karnataka India Delft Univ Technol Delft Netherlands

Motor vehicles significantly contribute to the escalating levels of air and noise pollution in urban centers worldwide. Numerous studies have established a strong correlation between vehicle exhaust emissions, noise levels, and various factors such as traffic flow rate, vehicle composition, fleet speed, as well as deceleration and acceleration speeds. This research monitors ambient air quality and noise levels in diverse city centers during peak hours, shedding light on the impact of vehicular activities. The study investigates into the intricate relationship between vehicular composition and the concentration of particulate matter (PM). Furthermore, it conducts a comprehensive analysis of how traffic composition influences roadside noise pollution, identifying key factors contributing to this environmental concern. Employing an efficient deep learning process, the research employs image detection and tracking of vehicles to enhance understanding. Additionally, various machine learning tools are applied for the prediction of traffic-related air and noise pollution. This research makes a significant contribution to sustainable transportation planning, offering valuable insights into the complex dynamics of vehicular impact on urban environments. The findings not only enhance our understanding of pollution sources but also pave the way for informed decision-making in developing strategies to mitigate the adverse effects of motor vehicle activities.

关键词： traffic engineering environmental aspects sustainability image processing real-time vehicle detection air pollution air quality meter noise pollution sound level meter sustainable development goal (SDG) 11: sustainable cities and communities

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DL-DARE: deep learning-based different activity recognition for the human-robot interaction environment

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NEURAL COMPUTING & APPLICATIONS 2023年第16期35卷 12029-12037页

作者： Kansal, Sachin Jha, Sagar Samal, Prathamesh Thapar Inst Engn Technol Patiala Comp Sci Engn Dept Patiala 147004 Punjab India

This paper proposes a deep learning-based activity recognition for the Human-Robot Interaction environment. The observations of the object state are acquired from the vision sensor in the real-time scenario. The activity recognition system examined in this paper comprises activities labeled as classes (pour, rotate, drop objects, and open bottles). The image processing unit processes the images and predicts the activity performed by the robot using deep learning methods so that the robot will do the actions (sub-actions) according to the predicted activity.

关键词： RGB sensor VGG-16 Resnet-50 Hyper-parameters deep learning Visual tracking Feature extraction

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Agry: a comprehensive framework for plant diseases classification via pretrained EfficientNet and convolutional neural networks for precision agriculture

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第24期83卷 64813-64851页

作者： Saleki, Sheida Tahmoresnezhad, Jafar Urmia Univ Technol Fac Informat Technol & Comp Engn Orumiyeh Iran

Diagnosing plant diseases is a vital issue in maintaining and developing of agricultural products. These diseases occur with changes in the tissue of different parts of plants. While the previous researches have only been conducted on certain species of plants and specific parts, we propose a comprehensive approach that has reached a high accuracy in diagnosing and classifying the disease of offending plant species by examining their different parts, including leaves, fruits, tree trunks, and seeds. We extract features from different layers of pre-trained AlexNet, ResNet50, VGG16, EfficientNetB0, EfficientNetB3 and EfficientNetB7 deep models with a spatial attention module to classify samples with an SVM classifier with RBF kernel. In order to automate the detection of plant diseases by manned or unmanned agricultural machines, our proposed Agry requires only 0.04089 seconds for image processing and decision making in real time. Also, due to the use of transfer learning, the cost of building the proposed model, including time and resources, is minimized. The results of the tests show a significant improvement compared to the previous works, and in most cases the classification is done without errors.

关键词： Precision agriculture Plant disease Machine vision deep learning Transfer learning deep convolutional neural networks Pre-trained models

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PlantView: Integrating deep learning with 3D modeling for indoor plant augmentation

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ECOLOGICAL INFORMATICS 2024年 84卷

作者： Afzal, Sitara Khan, Haseeb Ali Lee, Jong Weon Sejong Univ Dept Software Mixed Real & Interact Lab Seoul 05006 South Korea

Indoor plant recognition poses significant challenges due to the variability in lighting conditions, plant species, and growth stages. Despite the growing interest in applying deep learning techniques to plant data, there still needs to be more research focused on the automatic recognition of indoor plant species, highlighting the need for real-time, automated solutions. To address this gap, this study introduces a novel approach for real-time identification and visualization of indoor plants using a Convolutional Neural Network (CNN)-based model called PlantView, integrated with Augmented reality (AR) for enhanced user interaction. The proposed PlantView model not only accurately classifies the plant species but also visualizes them in a 3D AR environment, allowing users to interact with virtual plant models seamlessly integrated into their real-world surroundings. We developed a custom dataset comprising over 28,000 images of 48 different plant species at various growth stages, captured under diverse lighting conditions and camera settings. Our proposed approach achieves an impressive accuracy of 98.20 %. To validate the effectiveness of PlantView model, we conduct extensive experiments and compared its performance against state-of-the-art methods, demonstrating its superior accuracy and processing speed. The results indicate that our method is not only highly effective for real-time indoor plant recognition but also offers practical applications for enhancing indoor plant care and visualization. This research offers a comprehensive solution for indoor plant enthusiasts and professionals, combining advanced computer vision techniques with immersive AR visualization to revolutionize the way indoor plants are identified, visualized, and integrated into living spaces.

关键词： Indoor plant Computer vision image processing deep learning Augmented reality

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deep-learning based in-situ micrograph analysis of high-density crystallization slurry using image and data enhancement strategy

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POWDER TECHNOLOGY 2024年 437卷

作者： Li, Muyang Liu, Jian Yao, Tuo Gao, Zhenguo Gong, Junbo Tianjin Univ Coinnovat Ctr Chem & Chem Engn Tianjin Sch Chem Engn & Technol State Key Lab Chem Engn Tianjin 300072 Peoples R China Haihe Lab Sustainable Chem Transformat Tianjin 300192 Peoples R China

Process analytical technology (PAT) plays a crucial role in optimizing crystalline powder product qualities, improving process stability, and reducing experimental costs by enabling real-time monitoring and control of process variables during the crystallization process. In -situ process imaging and analysis have gained significant attention due to their capacity to provide abundant information through images, leading to remarkable advancements in image processing technology, particularly with the support of artificial intelligence. However, the performance suffers when processing high -density slurry images due to challenges such as defocusing, overlapping, background and crystal edges blurriness, and inconsistencies in crystal scales. To address these challenges and explore research strategies based on deep learning for segmenting and analyzing high slurry density images effectively, this study constructed a specific dataset that contains high -density slurry crystal images which is well -labeled, and introduced state-of-the-art neural networks including YOLOv8, U2 -net, and Mask R -CNN combined with image and data enhancement strategies. Comparative analysis revealed that YOLOv8 outperformed Mask R -CNN and U2 -net by capturing multi -dimensional information in high -density crystal slurry scenarios. Finally, based on the segmentation results of the practical taurine crystallization process using proposed strategy, 1D crystal size, aspect ratio along with 2D size distributions were extracted for further evaluation of crystallization kinetics and crystal properties accurately and quickly.

关键词： Solution crystallization image analysis deep-learning High density slurry Data enhancement

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deep learning for High Speed Optical Coherence Elastography With a Fiber Scanning Endoscope

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IEEE TRANSACTIONS ON MEDICAL IMAGING 2025年第3期44卷 1445-1453页

作者： Neidhardt, Maximilian Latus, Sarah Eixmann, Tim Huttmann, Gereon Schlaefer, Alexander Hamburg Univ Technol Inst Med Technologyand Intelligent Syst D-21073 Hamburg Germany Interdisciplinary CompetenceCenter Interface Res I D-20251 Hamburg Germany Univ Lubeck Inst Biomed Opt D-23562 Lubeck Germany SustAInLivWork Ctr Excellence LT-44249 Kaunas Lithuania Interdisciplinary Competence Ctr Interface Res ICC D-20251 Hamburg Germany

Tissue stiffness is related to soft tissue pathologies and can be assessed through palpation or via clinical imaging systems, e.g., ultrasound or magnetic resonance imaging. Typically, the image based approaches are not suitable during interventions, particularly for minimally invasive surgery. To this end, we present a miniaturized fiber scanning endoscope for fast and localized elastography. Moreover, we propose a deep learning based signal processing pipeline to account for the intricate data and the need for real-time estimates. Our elasticity estimation approach is based on imaging complex and diffuse wave fields that encompass multiple wave frequencies and propagate in various directions. We optimize the probe design to enable different scan patterns. To maximize temporal sampling while maintaining three-dimensional information we define a scan pattern in a conical shape with a temporal frequency of 5.05kHz. To efficiently process the image sequences of complex wave fields we consider a spatio-temporal deep learning network. We train the network in an end-to-end fashion on measurements from phantoms representing multiple elasticities. The network is used to obtain localized and robust elasticity estimates, allowing to create elasticity maps in real-time. For 2D scanning, our approach results in a mean absolute error of 6.31(576)kPa compared to 11.33(1278)kPa for conventional phase tracking. For scanning without estimating the wave direction, the novel 3D method reduces the error to 4.48(363)kPa compared to 19.75(2182)kPa for the conventional 2D method. Finally, we demonstrate feasibility of elasticity estimates in ex-vivo porcine tissue.

关键词： Imaging Probes Elasticity deep learning Phantoms Endoscopes Data acquisition Robots Optical fibers Elastography Elasticity mapping endoscopic imaging neural networks miniaturized probe OCT

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Exploring the Application of deep learning in Multi-View image Fusion in Complex Environments

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TRAITEMENT DU SIGNAL 2023年第6期40卷 2731-2740页

作者： Luo, Xiujuan Shao, Lili Heze Univ Sch Comp Heze 274015 Peoples R China

The advancement of technology has unveiled the immense potential of deep learning across various domains, notably in multi-view image fusion within complex environments. Multiview image fusion aims to merge images from different perspectives to garner more comprehensive and detailed information. Despite this, challenges persist in such fusion under complex conditions, particularly when confronting significant variations in perspective and intricate lighting scenarios. Predominant deep learning approaches, reliant on extensive annotated data, grapple with high computational complexity when processing large-scale and high-dimensional image data, thus hindering real-time applicability. This exploration primarily focuses on two facets: multi-view image registration based on the moment of inertia axis method, and multi-view image fusion utilizing morphological decomposition and attention feature integration. The objective is to enhance the efficiency and effectiveness of multi-view image fusion in complex settings, propelling the practical advancement of deep learning technologies.

关键词： deep learning multi-view image fusion complex environments moment of inertia axis method morphological decomposition attention feature integration

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A lightweight image inpainting model for removing unwanted objects from residential real estate's indoor scenes

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第34期83卷 80389-80410页

作者： Sompoppokasest, Srun Siriborvornratanakul, Thitirat Natl Inst Dev Adm Grad Sch Appl Stat 148 SeriThai Rd Bangkok 10240 Thailand

To enhance the appeal of residential real estate listings and captivate online customers, clean and visually convincing indoor scenes are highly desirable. In this research, we introduce an innovative image inpainting model designed to seamlessly replace undesirable elements within images of indoor residential spaces with realistic and coherent alternatives. While Generative Adversarial Networks (GANs) have demonstrated remarkable potential for removing unwanted objects, they can be resource-intensive and face difficulties in consistently producing high-quality outcomes, particularly when unwanted objects are scattered throughout the images. To empower small- and medium-sized businesses with a competitive edge, we present a novel GAN model that is resource-efficient and requires minimal training time using arbitrary mask generation and a novel half-perceptual loss function. Our GAN model achieves compelling results in removing unwanted elements from indoor scenes, demonstrating the capability to train within a single day using a single GPU, all while minimizing the need for extensive post-processing.

关键词： deep learning image inpainting image completion Generative Adversarial Network

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Crystal search - feasibility study of a real-time deep learning process for crystallization well images

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ACTA CRYSTALLOGRAPHICA A-FOUNDATION AND ADVANCES 2023年第4期79卷 331-338页

作者： Thielmann, Yvonne Luft, Thorsten Norbert, Zint A. Koepke, Juergen Max Planck Inst Biophys Mol Membrane Biol Max von Laue Str 3 D-60438 Frankfurt Germany Systrade GmbH Bockenheimer Landstr 47 D-60325 Frankfurt Germany

To avoid the time-consuming and often monotonous task of manual inspection of crystallization plates, a Python-based program to automatically detect crystals in crystallization wells employing deep learning techniques was developed. The program uses manually scored crystallization trials deposited in a database of an in-house crystallization robot as a training set. Since the success rate of such a system is able to catch up with manual inspection by trained persons, it will become an important tool for crystallographers working on biological samples. Four network architectures were compared and the SqueezeNet architecture performed best. In detecting crystals AlexNet accomplished a better result, but with a lower threshold the mean value for crystal detection was improved for SqueezeNet. Two assumptions were made about the imaging rate. With these two extremes it was found that an image processing rate of at least two times, but up to 58 times in the worst case, would be needed to reach the maximum imaging rate according to the deep learning network architecture employed for real-time classification. To avoid high workloads for the control computer of the CrystalMation system, the computing is distributed over several workstations, participating voluntarily, by the grid programming system from the Berkeley Open Infrastructure for Network Computing (BOINC). The outcome of the program is redistributed into the database as automatic real-time scores (ARTscore). These are immediately visible as colored frames around each crystallization well image of the inspection program. In addition, regions of droplets with the highest scoring probability found by the system are also available as images.

关键词： biocrystallization high-throughput screening deep learning neural network U-Net AlexNet VggNet ResNet SqueezeNet BOINC

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Adaptive routing sign transformer framework

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SIGNAL image AND VIDEO processing 2023年第4期17卷 1715-1722页

作者： Chen, Yuming Mei, Xue Nanjing Tech Univ Coll Elect Engn & Control Sci Nanjing Peoples R China

Although deep learning-based continuous sign language translation (CSLT) models have made great progress in recent years, they are still faced with various difficulties and limitations when applied to practical scenarios. In order to better apply the technology of deep learning, we propose the adaptive route sign transformer framework for CSLT. The adaptive routing strategy is proposed to solve the problem that the accuracy of the deep learning model trained in the laboratory scene is greatly reduced when it is applied to the real scene, and the back-end part of the model, we present, adopts transformer-style decoder architecture to real-time translate sentences from the spatiotemporal context around the signer. By means of network layer visualization, we demonstrate that the attention mechanism of the model captures the hand and face regions of signers, which is often crucial for semantic analysis of video sign language. In this paper, we introduce the Chinese sign language corpus of the business scene which show sign language communication in a bank, a station, etc. It has certain impetuses for further research on video sign language translation. Experiments are carried out the PHOENIX-Weather 2014T (RWTH Aachen University, Germany);the proposed model outperforms the state-of-the-art in inference times and accuracy using only raw RGB as input.

关键词： Sign language recognition Domain adaption Transfer learning Transformer

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