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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4354-4363页

作者： Roy, Ayush Mohiuddin, S.K. Sarkar, Ram Jadavpur University Department of Electrical Engineering Kolkata700032 India Asutosh College Department of Computer Science Kolkata700026 India Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

The process of modifying digital images has been made significantly easier by the availability of several image editing software. However, in a variety of contexts, including journalism, judicial processes, and historical documentation, the authenticity of images is of utmost importance. In particular, copy-move forgery is a distinct type of image manipulation, where a portion of an image is copied and pasted into another area of the same image, creating a fictitious or altered version of the original. In this research, we present a lightweight MultiResUnet architecture with the similarity-based positional attention module (SPAM) attention module for copy-move forgery detection (CMFD). By using a similarity measure across the patches of the features, this attention module identifies the patches, where a forged region is present. The lightweight network also aids in resource-efficient training and transforms the model into one that can be used in real time. We have employed four commonly used but extremely difficult CMFD datasets, namely CoMoFoD, COVERAGE, CASIA v2, and MICC-F600, to assess the effectiveness of our model. The proposed model significantly lowers false positives, thereby improving the pixel-level accuracy and dependability of CMFD tools. © 2020 IEEE.

关键词： real time systems

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Multi-TranResUnet: An Improved Transformer Network for Solving Multi-Scale Issues in image Segmentation

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IEEE ACCESS 2024年 12卷 129000-129011页

作者： Kang, Yajing Cheng, Shuai Guo, Liang Zheng, Chao Zhao, Jizhuang China Telecom Res Inst Beijing 102209 Peoples R China CAICT Inst Cloud Comp & Big Data Beijing 100191 Peoples R China

deep-learning-driven medical image segmentation marks a significant milestone in the evolution of intelligent healthcare systems. Despite remarkable accuracy achievements, real-world clinical applications still grapple with complex challenges, particularly in handling multi-scale medical targets. This paper introduces a novel and efficient medical image segmentation network that leverages Transformer technology. The proposed network utilizes the Transformer's global feature extraction capabilities, enriched with spatial context, to substantially elevate segmentation accuracy. Additionally, the fusion encoder we build by combining Transformer modules and Convolutional structures through feature fusion strategies can improve feature extraction capabilities. Acknowledging the computational demands of Transformer models in practical scenarios, we have meticulously optimized our Transformer architecture. This optimization focuses on reducing parameter complexity and inference latency, tailoring the model to address the typical sample scarcity in medical applications. We evaluated our model on two different medical datasets: the 2018 Lesion Boundary Segmentation Challenge, the 2018 Data Science Bowl Challenge and the Kvasir-Instrument dataset. Our model demonstrates state-of-the-art performance in both Dice and MIoU metrics, while maintaining robust real-time processing capabilities. Our code will be released at https://***/migouKang/Multi-TranResUnet.

关键词： Transformers image segmentation Feature extraction Medical diagnostic imaging Convolutional neural networks Accuracy Computational modeling Low latency communication Medical image segmentation deep learning transformer low-latency model

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Development of an Intelligent Weld Surface Defect Detection System: Design and Application

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IEEE INSTRUMENTATION & MEASUREMENT MAGAZINE 2025年第3期28卷 12-21页

作者： Wang, Guan-Qiang Zeng, He Chen, Ming-Song Lin, Yong-Cheng Zhang, Chi-Zhou Tan, Xian-Hua Guilin Univ Elect Sci & Technol Sch Mech & Elect Engn Guilin Peoples R China Natl Univ Def Technol Sch Intelligent Sci Changsha Peoples R China Cent South Univ Changsha Peoples R China Cent South Univ Light Alloy Res Inst Changsha Peoples R China Cent South Univ Sch Mech & Elect Engn Changsha Peoples R China Cent South Univ SLAM Deep Learning & Multisensory Fus Technol Nav Changsha Peoples R China

Early evaluation through the detection of surface defects in welds is essential to ensure the quality of weldments. In this study, considering the complexity of weld surface inspection, we designed a high-performance inspection system that combines image processing, defect detection, and model evolution functions. This system facilitates the engineering application of image super-resolution reconstruction, defect detection, and semi-supervised learning methods in real-world weld detection scenarios through the integrated weld surface inspection module and a visually interactive client. The experiment results demonstrate that the system can perform real-time processing, with a speed surpassing 30 frames per second, for super-resolution and detection evaluation of input images. Furthermore, it has autonomous learning capabilities, which continuously improve detection precision and reduce the miss rate during long-time detection.

关键词： Surface reconstruction Welding Superresolution Inspection Semisupervised learning real-time systems Surface treatment System analysis and design image reconstruction Defect detection Defect detection

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Multi-view image Fusion Using Ensemble deep learning Algorithm For MRI And CT images

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ACM TRANSACTIONS ON ASIAN AND LOW-RESOURCE LANGUAGE INFORMATION processing 2024年第3期23卷 1-24页

作者： Thenmoezhi, N. Perumal, B. Lakshmi, A. Kalasalingam Acad Res & Educ Dept Elect & Commun Engn Sch Elect Elect & Biomed Technol Krishnankoil 626126 Tamil Nadu India Ramco Inst Technol Dept Elect & Commun Engn Rajapalayam 626117 Tamil Nadu India

Medical image fusions are crucial elements in image-based health care diagnostics or therapies and generic applications of computer visions. However, the majority of existing methods suffer from noise distortion that affects the overall output. When pictures are distorted by noises, classical fusion techniques perform badly. Hence, fusion techniques that properly maintain information comprehensively from multiple faulty pictures need to be created. This work presents Enhanced Lion Swarm Optimization (ESLO) with Ensemble deep learning (EDL) to address the aforementioned issues. The primary steps in this study include image fusions, segmentation, noise reduction, feature extraction, picture classification, and feature selection. Adaptive Median Filters are first used for noise removal in sequence to enhance image quality by eliminating noises. The MRIs and CT images are then segmented using the Region Growing-based k-Means Clustering (RKMC) algorithm to separate the images into their component regions or objects. images in black and white are divided into image. In the white image, the RKMC algorithm successfully considered the earlier tumour probability. The next step is feature extraction, which is accomplished by using the Modified Principal Component Analysis (MPCA) to draw out the most informative aspects of the images. Then the ELSO algorithm is applied for optimal feature selection, which is computed by best fitness values. After that, multi-view image fusions of multi modal images derive lower-, middle-, and higher-level image contents. It is done by using deep Convolution Neural Network (DCNN) and the Tissue-Aware Conditional Generative Adversarial Network (TAcGAN) algorithm, which fuses the multi-view features and relevant image features, and it is used for real-time applications. ELSO +EDL algorithm gives better results in terms of accuracy, Peak Signal-To-Noise Ratio (PSNR), and lower Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE)

关键词： Multi-view image fusions Magnetic Resonance Imaging (MRI) images Computed Tomography (CT) images Enhanced Lion Swarm Optimization (ELSO) Ensemble deep learning (EDL) algorithm

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Lightweight lane line detection based on learnable cluster segmentation with self-attention mechanism

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IET INTELLIGENT TRANSPORT SYSTEMS 2023年第3期17卷 518-529页

作者： Yang, Qin Ma, Yahong Li, Linsen Su, Chang Gao, Yujie Tao, Jiaxin Huang, Zhentao Jiang, Rui Xijing Univ Sch Informat Engn Xian Peoples R China Autocore Ai Nanjing Peoples R China

Pixel segmentation is one of the most commonly used deep learning methods for modern lane line detection. Although deep segmentation outperforms traditional methods, there are two main problems: slow speed and limited receptive field. In response to these problems, this paper proposes a lightweight lane line detection algorithm based on learnable cluster segmentation and self-attention mechanism, which has extremely fast speed and the ability to adapt to real scenes. The lane detection process is considered as clustering under row segmentation. The data is processed through row segmentation and fed into a self-attention mechanism. In addition to the benchmark dataset for lane detection, the algorithm was ported to real vehicles for real-time road testing. Two tests show that our method performs very well on TuSimple, with an accuracy of 97.15%, an F1 score of 73.5 on CULane, and a speed of 142.7 frames per second (FPS), which solves the problem of slow cluster segmentation, while improving the accuracy of row segmentation. In the new scenario, the method has a misjudgment rate of only 6.7% for lane line points, which is suitable for the high standard requirements of autonomous driving.

关键词： traffic engineering computing image segmentation feature extraction slow cluster segmentation self-attention mechanism pattern clustering row segmentation extremely fast speed learning (artificial intelligence) lane line points lane detection process modern lane line detection Neural nets pixel segmentation Optical, image and video signal processing Computer vision and image processing techniques commonly used deep learning methods deep segmentation convolutional neural nets limited receptive field Traffic engineering computing object detection lightweight lane line detection algorithm deep learning (artificial intelligence) image recognition slow speed road vehicles learnable cluster segmentation

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FIRESTART: Fire Ignition Recognition with Enhanced Smoothing Techniques and real-time Tracking 22nd

FIRESTART: Fire Ignition Recognition with Enhanced Smoothing...

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22nd International Conference on image Analysis and processing (ICIAP)

作者： Zedda, Luca Loddo, Andrea Di Ruberto, Cecilia Univ Cagliari Dept Math & Comp Sci Cagliari Italy

ISBN: (纸本)9783031510229;9783031510236

Fires can potentially cause significant harm to both people and the environment. Recently, there has been a growing interest in real-time fire and smoke detection to provide practical assistance. Detecting fires in outdoor areas is crucial to safeguard human lives and the environment. This is especially important in situations where more than traditional smoke detectors may be required. In this work, we propose FIRESTART, which aims to achieve accurate and robust ignition detection for prompt identification and response to fire incidents. The proposed framework utilizes a lightweight deep learning architecture and post-processing techniques for fire-starting interval detection. Its evaluation was conducted on the ONFIRE dataset, comparing it with several state-of-the-art methods. The results are encouraging, particularly from computational and real-time use perspectives.

关键词： deep learning Computer Vision image processing Vision Transformers Fire Detection

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A deep learning Approach for Detecting Covid-19 Using the Chest X-Ray images

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Computers, Materials & Continua 2023年第1期74卷 751-768页

作者： Fatemeh Sadeghi Omid Rostami Myung-Kyu Yi Seong Oun Hwang Department of Industrial Engineering Sharif University of TechnologyTehran14588-89694Iran Department of Industrial Engineering University of HoustonHoustonTX77204USA Department of Computer Engineering Gachon UniversitySeongnam13120Korea

real-time detection of Covid-19 has definitely been the most widely-used world-wide classification problem since the start of the pandemic from 2020 until *** the meantime,airspace opacities spreads related to lung have been of the most challenging problems in this area.A common approach to do on that score has been using chest X-ray images to better diagnose positive Covid-19 *** to most other classification problems,machine learning-based approaches have been the first/most-used candidates in this *** schemes based on machine/deep learning have been proposed in recent years though increasing the performance and accuracy of the system has still remained an open *** this paper,we develop a novel deep learning architecture to better classify the Covid-19 X-ray *** do so,we first propose a novel multi-habitat migration artificial bee colony(MHMABC)algorithm to improve the exploitation/exploration of artificial bee colony(ABC)*** that,we optimally train the fully connected by using the proposed MHMABC algorithm to obtain better accuracy and convergence rate while reducing the execution *** experiment results on Covid-19 X-ray image dataset show that the proposed deep architecture has a great performance in different important optimization ***,it will be shown that the MHMABC algorithm outperforms the state-of-the-art algorithms by evaluating its performance using some wellknown benchmark datasets.

关键词： ChestX-ray image processing evolutionary deep learning covid-19

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Multi-aspect detection and classification with multi-feed dynamic frame skipping in vehicle of internet things

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WIRELESS NETWORKS 2024年第5期30卷 4543-4554页

作者： Ahmed, Usman Lin, Jerry Chun-Wei Srivastava, Gautam Western Norway Univ Appl Sci Dept Comp Sci Elect Engn & Math Sci N-5063 Bergen Norway Brandon Univ Dept Math & Comp Sci Brandon MB Canada China Med Univ Res Ctr Interneural Comp Taichung 40402 Taiwan

Consumer demand for automobiles is changing because of the vehicle's dependability and utility, and the superb design and high comfort make the vehicle a wealthy object class. The creation of object classes necessitates the creation of more sophisticated computer vision models. However, the critical issue is image quality, determined by lighting conditions, viewing angle, and physical vehicle construction. This work focuses on creating and implementing a deep learning-based traffic analysis system. Using a variety of video feeds and vehicle information, the developed model recognizes, categorizes, and counts vehicles in real-time traffic flow. The dynamic skipping method offered in the developed model speeds up the processing of a lengthy video stream while ensuring that the video picture is delivered accurately to the viewer. In real-time traffic, standard vehicle retrieval may assist in determining the make, model, and year of the vehicle. Previous MobileNet and VGG19 models achieved F-values of 0.81 and 0.91, respectively. However, the proposed solution raises MobileNet's frame rate from 71.2 to 89.17 and VGG19's frame rate from 48.2 to 59.14. The method may be applied to a wide range of applications that require a dedicated zone to monitor real-time data analysis and normal multimedia operations.

关键词： Vehicle classification deep learning Fine-grained classification Vehicular traffic Sensor data

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Effective First-Break Picking of Seismic Data Using Geometric learning Methods

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REMOTE SENSING 2025年第2期17卷 232-232页

作者： Wen, Zhongyang Ma, Jinwen Peking Univ Sch Math Sci Dept Informat & Computat Sci Beijing 100871 Peoples R China Peking Univ LMAM Beijing 100871 Peoples R China

Automatic first-break(FB) picking is a key task in seismic data processing, with numerous applications in the field. Over the past few years, both unsupervised and supervised learning algorithms have been applied to 2D seismic arrival time picking and obtained good picking results. In this paper, we introduce a strategy of optimizing certain geometric properties of the target curve for first-break picking which can be implemented in both unsupervised and supervised learning modes. Specifically, in the case of unsupervised learning, we design an effective curve evolving algorithm according to the active contour(AC) image segmentation model, in which the length of the target curve and the fitting region energy are minimized together. It is interpretable, and its effectiveness and robustness are demonstrated by the experiments on real world seismic data. We further investigate three schemes of combining it with human interaction, which is shown to be highly useful in assisting data annotation or correcting picking errors. In the case of supervised learning especially for deep learning(DL) models, we add a curve loss term based on the target curve geometry of first-break picking to the typical loss function. It is demonstrated by various experiments that this curve regularized loss function can greatly enhance the picking quality.

关键词： first-break picking seismic data active contours level-set deep learning semantic segmentation

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Intelligent real-life key-pixel image detection system for early Arabic sign language learners

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2063页

作者： Alamri, Faten S. Rehman, Amjad Abdullahi, Sunusi Bala Saba, Tanzila Princess Nourah Bint Abdulrahman Univ Coll Sci Dept Math Sci Riyadh Saudi Arabia Prince Sultan Univ Artificial Intelligence & Data Analyt Lab AIDA CCI Riyadh Saudi Arabia King Mongkuts Univ Technol Thonburi Fac Engn Dept Elect & Telecommun Engn Bangkok Thailand

Lack of an effective early sign language learning framework for a hard -of -hearing population can have traumatic consequences, causing social isolation and unfair treatment in workplaces. Alphabet and digit detection methods have been the basic framework for early sign language learning but are restricted by performance and accuracy, making it difficult to detect signs in real life. This article proposes an improved sign language detection method for early sign language learners based on the You Only Look Once version 8.0 (YOLOv8) algorithm, referred to as the intelligent sign language detection system (iSDS), which exploits the power of deep learning to detect sign language -distinct features. The iSDS method could overcome the false positive rates and improve the accuracy as well as the speed of sign language detection. The proposed iSDS framework for early sign language learners consists of three basic steps: (i) image pixel processing to extract features that are underrepresented in the frame, (ii) inter -dependence pixel -based feature extraction using YOLOv8, (iii) web -based signer independence validation. The proposed iSDS enables faster response times and reduces misinterpretation and inference delay time. The iSDS achieved state-of-the-art performance of over 97% for precision, recall, and F1 -score with the best mAP of 87%. The proposed iSDS method has several potential applications, including continuous sign language detection systems and intelligent web -based sign recognition systems.

关键词： Computer vision End-to-end deep neural network Early sign language learning Sign language detection Hand gestures Pattern recognition Natural language processing

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