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Conference on real-time image processing and deep learning

作者： Kasinets, Dzmitry Saeed, Amir K. Johnson, Benjamin A. Rodriguez, Benjamin M. Johns Hopkins Univ Whiting Sch Engn 3400 N Charles St Baltimore MD 21218 USA

ISBN: (纸本)9781510673878;9781510673861

In the context of the advancing digital landscape, there is a discernible demand for robust and defensible methodologies in addressing the challenges in multi-class image classification. The evolution of intelligent systems mandates swift evaluations of environmental variables to facilitate decision-making within an authorized workflow. Recognizing the imperative role of ensemble models, this paper undertakes an exploration into the efficacy of layered Convolutional Neural Network (CNN) architectures for the nuanced task of multi-class image classification, specifically applied to traffic signage recognition in the dynamic context of a moving vehicle. The research methodology employs a YOLO (You Only Look Once) model to establish a comprehensive training and testing dataset. Subsequently, a stratified approach is adopted, leveraging layered CNN architectures to categorize clusters of objects and, ultimately, extrapolate the pertinent speed limit values. Our endeavor aims to elucidate the procedural framework for integrating CNN models, providing insights into their accuracy within the application domain.

关键词： convolutional neural networks transforms architectures modeling classification

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Sensor-Based Gymnastics Action Recognition Using time-Series images and a Lightweight Feature Fusion Network

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IEEE SENSORS JOURNAL 2024年第24期24卷 42573-42583页

作者： Wang, Wanyue Lian, Chao Zhao, Yuliang Zhan, Zhikun Northeastern Univ Qinhuangdao Sch Business Qinhuangdao 066004 Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China Hebei Key Lab Micronano Precis Opt Sensing & Measu Qinhuangdao 066004 Peoples R China

With the development of micro-electromechanical systems (MEMSs) and artificial intelligence technology, the application of wearable devices in human motion capture and recognition has gradually become a research hotspot. However, existing action recognition methods based on wearable sensors still face issues such as limited feature extraction capability and insufficient information utilization, leaving significant room for improvement in recognition accuracy. To address these challenges, this article proposes a motion recognition method based on time-series images and a lightweight feature fusion network. First, two time-series-to-image conversion methods, raw sequence image (RI) and raw sequence change image (RCI), are proposed, which fully leverage the advantages of convolutional neural networks (CNNs) in image processing. Second, a dual-channel feature fusion network is designed, enhancing the ability to extract features of gymnastic movements through the selection of backbone networks and the design of feature fusion modules. Finally, the effectiveness of the proposed method in gymnastics action recognition is validated. The experimental results show that the proposed method achieves an accuracy of 99.35%, which is at least 4.77% higher than existing machine learning methods and at least 2.03% higher than advanced deep learning methods. This demonstrates a significant improvement in recognition accuracy, proving the effectiveness and superiority of the proposed method in human action recognition. This method is expected to be extended to more application scenarios and provide technical support for the development of wearable devices.

关键词： Feature extraction Sensors Accuracy image recognition deep learning Convolutional neural networks Machine learning algorithms time-domain analysis Hidden Markov models Wearable sensors Action recognition deep learning image representation sensor data processing wearable sensors

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A Review of Intrusion Detection for Railway Perimeter Using deep learning-Based Methods

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IEEE ACCESS 2024年 12卷 184142-184157页

作者： Wang, Jin Zhai, Hongyang Yang, Yang Xu, Niuqi Li, Hao Fu, Di Beijing Univ Technol Beijing Key Lab Traff Engn Beijing 100124 Peoples R China Beijing Univ Technol Beijing Engn Res Ctr Urban Transport Operat Guaran Beijing 100124 Peoples R China

Efficiently detecting intrusions on a railway perimeter is crucial for ensuring the safety of railway transportation. With the development of computer vision, researchers have been actively exploring methods for detecting foreign object intrusion via image recognition technology. This article reviews the background and importance of detecting railway perimeter intrusion, summarizes the limitations of traditional detection methods, and emphasizes the potential of improving detection accuracy and efficiency in image recognition with deep learning models. Further, it introduces the development of deep learning in image recognition, focusing on the principles and progress of key technologies such as convolutional neural networks (CNNs) and vision transformers (ViTs). In addition, the application status of semantic segmentation and object detection algorithms based on deep learning in detecting railway perimeter intrusion is explored, including the classification, principles, and performance of the algorithms in practical applications. Finally, it highlights the primary challenges faced in railway perimeter intrusion detection and projects future research directions to resolve these challenges, including multisource data fusion, large-scale dataset construction, model compression, and end-to-end multitask learning networks. These studies support the accuracy and real-time detection of railway perimeter intrusion, and provide technical guarantees for railway transportation monitoring tasks.

关键词： Rail transportation Convolutional neural networks deep learning Monitoring Feature extraction Accuracy Kernel Accidents Computer architecture Computational modeling Railway semantic segmentation object detection foreign object intrusion railway safety

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Efficient Ship:A Hybrid deep learning Framework for Ship Detection in the River

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Computer Modeling in Engineering & Sciences 2024年第1期138卷 301-320页

作者： Huafeng Chen Junxing Xue Hanyun Wen Yurong Hu Yudong Zhang School of Computer Engineering Jingchu University of TechnologyJingmen448000China School of Computer Science Yangtze UniversityJingzhou434023China School of Computing and Mathematic Sciences University of LeicesterLeicesterLE17RHUK

Optical image-based ship detection can ensure the safety of ships and promote the orderly management of ships in offshore *** deep learning researches on optical image-based ship detection mainly focus on improving one-stage detectors for real-time ship detection but sacrifices the accuracy of *** solve this problem,we present a hybrid ship detection framework which is named EfficientShip in this *** core parts of the EfficientShip are DLA-backboned object location(DBOL)and CascadeRCNN-guided object classification(CROC).The DBOL is responsible for finding potential ship objects,and the CROC is used to categorize the potential ship *** also design a pixel-spatial-level data augmentation(PSDA)to reduce the risk of detection model *** compare the proposed EfficientShip with state-of-the-art(SOTA)literature on a ship detection dataset called *** show our ship detection framework achieves a result of 99.63%(mAP)at 45 fps,which is much better than 8 SOTA approaches on detection accuracy and can also meet the requirements of real-time application scenarios.

关键词： Ship detection deep learning data augmentation object location object classification

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An effective IoT interface considering an eye-tracking method for autonomous vehicle

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INTERNET OF THINGS 2025年 31卷

作者： Park, Junghoon Ajou Univ Coll Comp & Informat Dept Appl Artificial Intelligence WorldCup Ro 206 Suwon 16499 Gyeonggi Do South Korea

The number of Internet-connected devices is steadily increasing, exceeding 25 billion globally and projected to surpass 50 billion about 6.5 times the world population. At the core of IoT is data collection via sensors, forming big data for AI-driven analysis, optimization, and visualization. A convenient control environment is essential for devices like autonomous vehicles, requiring new interfaces such as touchless eye movement. This research proposes a real-time eye-tracking method using a single web camera, easily installed in cars and integrated with IoT. The system detects gaze by recognizing iris shape, enabling software-only tracking without extra hardware. Experiments show a mean absolute error (MAE) of 3.49 degrees, ensuring accuracy even with head movement. Unlike existing infrared (IR) LED or head-mounted methods, this approach offers a cost-effective, real-time solution. Using lightweight image processing instead of deep learning, the system achieves real-time tracking with low latency, making it ideal for low-power IoT and autonomous vehicles. It is expected to become a next-generation input interface for these applications.

关键词： Autonomous system Eye gaze tracking IoT image processing Interpolation real-time processing

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Road Intersection Analysis: Integrating image processing into Digital Twin Technologies

Road Intersection Analysis: Integrating Image Processing int...

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Conference on real-time processing of image, Depth, and Video Information

作者： Vdzquez Donaire, Francisco C. Abalo-Garcia, Alejandra Montemayor, Antonio S. Pantrigo, Juan J. Univ Rey Juan Carlos Calle Tulipan S-N Mostoles Spain

ISBN: (纸本)9781510673199;9781510673182

We present a real-time system for vehicle detection and classification in road intersections, incorporating image processing techniques. This system estimates the traffic flow at a specific point, as it is capable of recognizing the trajectories of different vehicles at an intersection, inferring whether they leave or enter the city. It is designed to be integrated into a high-fidelity digital twin, aiding in estimating environmental traffic pollutants. Since Computational Fluid Dynamics (CFD) use estimators like average or aggregate measurements, we use more accurate methods to estimate pollution. The implications of our study are significant for urban planning and traffic management. It allows for immediate decisions and informs long-term infrastructure planning by providing a deep understanding of intersection dynamics. Our research offers a comprehensive perspective on traffic analysis, introducing data-driven traffic management strategies for efficient urban mobility. The code developed for this purpose can be found in https://***/capo- urjc/TrackingSORT

关键词： deep learning digital twins traffic monitoring visual tracking

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RT-CBAM: Refined Transformer Combined with Convolutional Block Attention Module for Underwater image Restoration

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SENSORS 2024年第18期24卷 5893页

作者： Ye, Renchuan Qian, Yuqiang Huang, Xinming Jiangsu Univ Sci & Technol Sch Ocean Engn Dept Elect Informat Engn Zhenjiang 212003 Peoples R China

Recently, transformers have demonstrated notable improvements in natural advanced visual tasks. In the field of computer vision, transformer networks are beginning to supplant conventional convolutional neural networks (CNNs) due to their global receptive field and adaptability. Although transformers excel in capturing global features, they lag behind CNNs in handling fine local features, especially when dealing with underwater images containing complex and delicate structures. In order to tackle this challenge, we propose a refined transformer model by improving the feature blocks (dilated transformer block) to more accurately compute attention weights, enhancing the capture of both local and global features. Subsequently, a self-supervised method (a local and global blind-patch network) is embedded in the bottleneck layer, which can aggregate local and global information to enhance detail recovery and improve texture restoration quality. Additionally, we introduce a multi-scale convolutional block attention module (MSCBAM) to connect encoder and decoder features;this module enhances the feature representation of color channels, aiding in the restoration of color information in images. We plan to deploy this deep learning model onto the sensors of underwater robots for real-world underwater image-processing and ocean exploration tasks. Our model is named the refined transformer combined with convolutional block attention module (RT-CBAM). This study compares two traditional methods and six deep learning methods, and our approach achieved the best results in terms of detail processing and color restoration.

关键词： deep learning transformer image restoration

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An Enhanced Lung Cancer Detection Approach Using Dual-Model deep learning Technique

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 835-867页

作者： Sumaia Mohamed Elhassan Saad Mohamed Darwish Saleh Mesbah Elkaffas Department of Information Technology Institute of Graduate Studies and ResearchAlexandria UniversityAlexandria21526Egypt College of Computing and Information Technology Arab Academy for ScienceTechnology and Maritime TransportAlexandriaP.O.Box 1029Egypt

Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic *** detection of lung tumors significantly increases the chances of successful treatment and ***,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung ***-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate ***,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor *** overcome these disadvantages,dual-model or multi-model approaches can be *** research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of *** automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung ***8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single *** is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to ***,through the utilization of cutting-edge data augmentation methods,such as deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive *** combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applicati

关键词： Lung cancer detection dual-model deep learning technique data augmentation CNN YOLOv8

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Construction and real time Update Mechanism of a Multi User Personalized Electricity Consumption Behavior Model Under deep learning 2

Construction and Real Time Update Mechanism of a Multi User ...

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2nd IEEE International Conference on image processing and Computer Applications, ICIPCA 2024

作者： Gao, Chen Zhan, Shian Lin, Yinrong Zhu, Enguo Liu, Yan Zheng, Xiaohui Hong, Shouli State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fujian Fuzhou350003 China China Electric Power Research Institute Beijing100192 China

ISBN: (纸本)9798350360240

It is a troublesome problem to model the user's electricity consumption behavior and add an update mechanism. Traditional models have challenges in real-time and personalization, so the introduction of real-time update mechanism has become a key method to improve model performance. Based on deep learning technology, this research constructs a multi-user personalized electricity consumption behavior model, and introduces a real-time update mechanism. First, a large number of user electricity consumption behavior data is used for training to establish the predictive power of the model, and then through the introduction of a real-time update mechanism, the model can respond to new data in a timely manner, and perform parameter updates and prediction result generation. Through experiments, it is evaluated that the prediction accuracy rate of this method is between 89% and 98%. The electricity consumption behavior model with a real-time update mechanism is better than the model without a real-time update mechanism in terms of prediction accuracy and real-time performance. © 2024 IEEE.

关键词： Electricity Consumption Behavior Feature Extraction real time Update Mechanism RNN

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Recent Advances in deep-learning-Based SAR image Target Detection and Recognition

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6884-6915页

作者： Lang, Ping Fu, Xiongjun Dong, Jian Yang, Huizhang Yin, Junjun Yang, Jian Martorella, Marco Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Beijing Inst Technol Sch Integrated Circuits & Elect Beijing 100081 Peoples R China Nanjing Univ Sci & Technol Sch Elect & Opt Engn Nanjing 210094 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Pisa Dept Informat Engn I-56127 Pisa Italy

Synthetic aperture radar (SAR) image target detection and recognition (SAR-TDR) tasks have become research hot spots in the remote sensing application. These targets include ships, vehicles, aircraft, oil tanks, bridges, and so on. However, with the rapid development of SAR technology and increasingly complex electromagnetic environment, complex characteristics of SAR images bring severe challenges to the accurate SAR-TDR via traditional physical models or manual feature-extraction-based machine learning methods. In recent years, deep learning (DL), as a powerful automatic feature extraction algorithm, has been widely used in the computer vision domain. More specifically, DL has also been introduced into the SAR-TDR tasks and has effectively achieved good performance in terms of accuracy, real-time processing, etc. With the rapid development of DL, SAR image processing, and practical requirements of SAR-TDR in civilian and military domains, it is crucial to conduct a systematic survey on SAR-TDR in the past few years. In this survey article, we mainly conduct a systematic overview of DL-based SAR-TDR literature on two tasks, i.e., target recognition (e.g., ground vehicles, ships, and aircraft) and target detection (e.g., ships, aircraft, change detection, sea surface oil spills, and oil tanks). More specifically, our related works about these topics are also presented to verify the effectiveness of DL-based methods. First, several DL methods (e.g., convolutional neural networks, recurrent neural networks, autoencoders, and generative adversarial networks), commonly used in SAR-TDR, are briefly introduced. Then, a systematic review of DL-based SAR-TDR (including our related works) is presented. Finally, the current challenges and future possible research directions are deeply analyzed and discussed.

关键词： Radar polarimetry Target recognition Synthetic aperture radar Marine vehicles Feature extraction Object detection Aircraft Surveys Accuracy deep learning Automatic target recognition change detection (CD) deep learning (DL) SAR image interpretation target detection

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