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Enhanced image classification using edge CNN (E-CNN)

VISUAL COMPUTER 2024年第1期40卷 319-332页

作者： Aldin, Shaima Safa Aldin, Noor Baha Aykac, Mahmut Al Nahrain Univ Continuing Educ Ctr Baghdad Iraq Hasan Kalyoncu Univ Dept Elect & Elect Engn Gaziantep Turkiye Gaziantep Univ Dept Elect & Elect Engn Gaziantep Turkiye

Recently, deep learning has become a hot topic in wide fields, especially in the computer vision that proved its efficiency in processing images. However, it tends to overfit or consumes a long learning time in many platforms. The causes behind these issues return to the huge number of learning parameters and lack or incorrect training samples. In this work, two levels of deep convolutional neural network (DCNN) are proposed for classifying the images. The first one is enhancing the training images with removing unnecessary details, and the second one is detecting the edges of the processed images for further reduction of learning time in the DCNN. The proposed work is inspired by the human eye's way in recognizing an object, where a piece of object can be helpful in the recognition and not necessarily the whole object or full colors. The goal is to speed up the learning process of CNN based on the preprocessed training samples that are precise and lighter to work well in real-time applications. The obtained results proved to be more significant for real-time classification as it reduced the learning process by (94%) in Animals10 dataset with a validation accuracy of (99.2%) in accordance with the classical DCNNs.

关键词： CNN Classification Training time Edge

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Surface defect detection method of aluminium alloy castings based on data enhancement and CRT-DETR

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IET image processing 2024年第13期18卷 4275-4286页

作者： Li, Long Gao, Mingyang Tian, Xing Wang, Chengjun Yu, Jiangli Anhui Univ Sci & Technol Coll Artificial Intelligence 168 Taifeng Ave Huainan 232001 Anhui Peoples R China Anhui Univ Sci & Technol Affiliated Hosp 1 Huainan Peoples Hosp 1 Huainan Anhui Peoples R China

The surface quality of aluminium alloy castings is crucial to quality control. Aiming to address the challenges of limited samples and extensive computation in deep learning-based surface defect detection for aluminium alloy castings, this paper proposes a surface defect detection method based on data enhancement and the Casting real-time DEtection TRansformer. First, to tackle the issue of small sample sizes and uneven distribution in surface defect data sets of aluminium alloy castings, ECA-MetaAconC deep Convolution Generative Adversarial Networks is proposed for generating defects with fewer samples and employ the image augmentation (IMGAUG) library for sample enhancement. Second, building upon the real-time DEtection TRansformer (RT-DETR), a lightweight partial-rep convolution is designed to decrease the network's parameter count. Simultaneously, the Deformable attention module and the DRBC3 module are introduced to enhance the neck network, thereby improving the model's capability to capture information and enhancing its detection performance. Compared to RT-DETR, this method reduces the number of model parameters by 38.7%, increases mAP by 1.5%, and achieves a frame rate that is 1.58 times higher than the original model. The experimental results demonstrate that this method can effectively and accurately detect surface defects in aluminium alloy castings, satisfying industrial requirements. The surface quality of aluminium alloy castings is very important in quality control. Aiming at the problems of few samples, large amount of calculation, and poor real-time performance in the surface defect detection method of aluminium alloy castings based on deep learning, here a surface defect detection method based on data enhancement and Casting real-time DEtection TRansformer is designed. image

关键词： computer vision image processing

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real-time Large-Motion Deblurring for Gimbal-Based Imaging Systems

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL processing 2024年第3期18卷 346-357页

作者： Varghese, Nisha Rajagopalan, A. N. Ansari, Zahir Ahmed Indian Inst Technol Madras Dept Elect Engn Chennai 600036 India IRDE DRDO Dehra Dun 248001 India

Robotic systems employed in tasks such as navigation, target tracking, security, and surveillance often use camera gimbal systems to enhance their monitoring and security capabilities. These camera gimbal systems undergo fast to-and-fro rotational motion to surveil the extended field of view (FOV). A high steering rate (rotation angle per second) of the gimbal is essential to revisit a given scene as fast as possible, which results in significant motion blur in the captured video frames. real-time motion deblurring is essential in surveillance robots since the subsequent image-processing tasks demand immediate availability of blur-free images. Existing deep learning (DL) based motion deblurring methods either lack real-time performance due to network complexity or suffer from poor deblurring quality for large motion blurs. In this work, we propose a Gyro-guided Network for real-time motion deblurring (GRNet) which makes effective use of existing prior information to improve deblurring without increasing the complexity of the network. The steering rate of the gimbal is taken as a prior for data generation. A contrastive learning scheme is introduced for the network to learn the amount of blur in an image by utilizing the knowledge of blur content in images during training. To the GRNet, a sharp reference image is additionally given as input to guide the deblurring process. The most relevant features from the reference image are selected using a cross-attention module. Our method works in real-time at 30 fps. As a first, we propose a Gimbal Yaw motion real-wOrld (GYRO) dataset of infrared (IR) as well as color images with significant motion blur along with the inertial measurements of camera rotation, captured by a gimbal-based imaging setup where the gimbal undergoes rotational yaw motion. Both qualitative and quantitative evaluations on our proposed GYRO dataset, demonstrate the practical utility of our method.

关键词： Robotic systems gimbal-based imaging real-time motion deblurring gyro-aided motion deblurring video surveillance reference-based motion deblurring contrastive learning Robotic systems gimbal-based imaging real-time motion deblurring gyro-aided motion deblurring video surveillance reference-based motion deblurring contrastive learning

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COVID-19 Social Distancing IOT Application Using image processing And deep learning 1

COVID-19 Social Distancing IOT Application Using Image Proce...

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1st International Conference on Technologies for Smart Green Connected Society 2021, ICTSGS 2021

作者： Naga Swathi, T. Megala, V. Yokesh babu, S. Nath, Malaya Kumar Jayadev Rezin, U. Department of Electronics and Communication Engineering SRM Institute of Science and Technology Ramapuram Chennai600089 India Department of Electronics and Communication Engineering NIT Puducherry609609 India VIT University Vellore India

ISBN: (纸本)9781607685395

The Internet of Things is now a groundbreaking area with the potential to influence our lives and bring major changes to the globe. Many IoT approaches have been proposed to enable data-driven and smart applications for users.;therefore, architecture, applications, and technological systems must be created for fast and prompt enforcement of guidelines, regulations, and government directives to control such future epidemics. This paper describes a new architecture, possible use-cases, and some future possibilities for building such applications utilizing Smart City. The COVID-19 epidemic is an existing pandemic induced by that of the *** is a severe acute respiratory syndrome (SARS-CoV-2). It was discovered for the first time in December 2019 in Wuhan, China. Social distancing is a technique for controlling the transmission of infectious illnesses. It suggests that individuals must physically separate away from each other, decreasing intimate contact and therefore lowering the spreading of a deadly virus also including coronavirus. © The Electrochemical Society

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Edge Enhancing Based Blind Kernel Estimation for deep image Deblurring

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CIRCUITS SYSTEMS AND SIGNAL processing 2025年第2期44卷 1017-1044页

作者： Yang, Chunyu Wang, Weiwei Xian Univ Architecture & Technol Coll Sci Xian 710071 Shaanxi Peoples R China Shenzhen Univ Coll Math & Stat Shenzhen 518060 Guangdong Peoples R China

Blind image deblurring problem needs to estimate both the latent image and the blur kernel, which is an active and challenging task in image processing and computer vision. To tackle this ill-posed problem, various regularization techniques have been developed. In this work, we propose an edge-enhancing regularization-based blind kernel estimation model. Our work is inspired by the interesting observation that the gradient amplitude over the real edge will diminish after blurring process while it will increase in the smooth region near the real edge. The novelty is the gradient-based regularity, which is highly effective in recovering image edges, and facilitating kernel estimation. To solve the proposed model, we present an alternating iterative algorithm, which shows good numerical convergence. After estimating the blur kernel, we adopt existing excellent deep learning-based non-blind image deblurring techniques to obtain the final deblurring results. Experiments on extensive datasets show that our method can achieve the state-of-the-art performance among the related blind kernel estimation methods.

关键词： Blind image deblurring Edge enhancing regularization Kernel estimation deep method

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Complementary spatial transformer network for real-time 3D object recognition

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JOURNAL OF real-time image processing 2023年第5期20卷 88页

作者： Krishna Kumar, K. P. Paul, Varghese APJ Abdul Kalam Technol Univ CET Campus Thiruvananthapuram 695016 Kerala India Rajagiri Sch Engn & Technol Dept Comp Sci & Engn Kochi 682039 Kerala India

Tiny deep learning Models offer many advantages in various applications. From the perspective of statistical machine learning theory the contributions of this paper is to complement the research advances and results obtained so far in real-time 3D object recognition. We propose a Tiny deep learning Model named Complementary Spatial Transformer Network (CSTN) for real-time 3D object recognition. It turns out that CSTN's working, and analysis are much simplified in a target space setting. We make algorithmic enhancements to perform CSTN computations faster and keep the learning part of CSTN in minimal size. Finally, we provide the experimental verifications of the results obtained in publicly available point cloud data sets ModelNet40 and ShapeNetCore with our model performing 1.65-2 times better in DPS (Detections/s) rate on GPU hardware for 3D object recognition, when compared to state-of-the-art networks. Complementary Spatial Transformer Network architecture requires only 10-35% of trainable parameters, when compared to state-of-the-art networks, making the network easier to deploy in edge AI devices.

关键词： 3D object recognition Spatial transformer network Spatial entropy Target space real-time tiny deep learning models

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Evaluating image DNA Techniques for Filtering Unauthorized Content in Large-Scale Social Platforms

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APPLIED SCIENCES-BASEL 2025年第8期15卷 4539-4539页

作者： Cho, Kyungwoon Bahn, Hyokyung Ewha Womans Univ Embedded Software Res Ctr Seoul 03760 South Korea Ewha Womans Univ Dept Comp Engn Seoul 03760 South Korea

Featured Application This work can be applied to enhance the robustness of image filtering systems in large-scale content platforms, specifically for detecting unauthorized images and their transformed versions, preventing the dissemination of manipulated *** image filtering systems have become essential in large-scale content platforms to prevent the dissemination of unauthorized data. While extensive research has focused on identifying images based on categories or visual similarity, the filtering problem addressed in this study presents distinct challenges. Specifically, it involves a predefined set of filtering images and requires real-time detection of whether a distributed image is derived from an unauthorized source. Although three major approaches-bitmap-based, image processing-based, and deep learning-based techniques-have been explored, no comprehensive comparison has been conducted. To bridge this gap, we formalize the concept of image equivalence and introduce performance metrics tailored for fair evaluation. Through extensive experiments, we derive the following key findings. First, bitmap-based methods are practically viable in real-world scenarios, offering reasonable detection rates and fast search speeds even under resource constraints. Second, despite their success in tasks such as image classification, deep learning-based methods underperform in our problem domain, highlighting the need for customized models and architectures. Third, image processing-based techniques demonstrate superior performance across all key metrics, including execution time and detection rates. These findings provide valuable insights into designing efficient image filtering systems for diverse content platforms, particularly for detecting unauthorized images and their transformations effectively.

关键词： image DNA fingerprinting unauthorized content image filtering system image equivalence

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Innovative Quantum PlasmoVision-Based Imaging for real-time deepfake Detection

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PLASMONICS 2025年 1-17页

作者： Maheshwari, R. Uma Jayasudha, A. R. Pandey, Binay Kumar Pandey, Digvijay Hindusthan Inst Technol Dept Elect & Commun Engn Coimbatore India Hindusthan Coll Engn & Technol Dept Comp Applicat Coimbatore India Govind Ballabh Pant Univ Agr & Technol Pantnagar Coll Technol Dept Informat Technol Pantnagar Udham Singh Nagar Uttarakhand India Dept Tech Educ Uttar Pradesh Kanpur India

In recent years, the proliferation of deepfake images has posed a substantial threat to media credibility, security, and privacy. Contemporary detection techniques, predominantly reliant on deep learning algorithms, fail to identify the nuanced pixel-level discrepancies inherent in deepfake material. This study introduces PlasmoVision, an innovative quantum-enhanced plasmonic imaging technology that incorporates AI-driven deep learning for highly sensitive real-time deepfake detection. deepfakes alter digital images and videos to produce very persuasive fraudulent content, rendering traditional detection methods ineffective. Plasmonic surface resonance technology, in conjunction with quantum dots, has the capacity to capture intricate image features that can disclose such alterations. Integrating deep learning into this detection system improves the accuracy and velocity of analysis. The PlasmoVision technology employs quantum dot-enhanced plasmonic arrays to detect sub-pixel-level resonance shifts resulting from light interaction with the image surface. The optical signals are analyzed with a sophisticated convolutional neural network (CNN) that categorizes images according to the plasmonic resonance data. The AI model is trained on a varied dataset of genuine and deepfake photos, attaining an ideal equilibrium between detection sensitivity and speed. real-time picture analysis is accomplished by swift plasmonic scanning and AI-driven classification. The suggested device attained an accuracy rate of 98.6% in identifying deepfakes within a test dataset, exhibiting a false positive rate of 1.2% and a false negative rate of 0.5%. The quantum-enhanced plasmonic system identified pixel abnormalities with a sensitivity of up to 10 nm, markedly surpassing conventional deepfake detection technologies. PlasmoVision real-time analysis capacity decreased processing time by 35% relative to traditional approaches, rendering it exceptionally appropriate for extensive and real-ti

关键词： Quantum dots Plasmonic surface resonance deepfake detection AI-driven deep learning Convolutional neural networks image authentication real-time analysis Sub-pixel detection Media security Optical sensing

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deep learning and Machine learning for Malaria Detection: Overview, Challenges and Future Directions

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INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY & DECISION MAKING 2024年第5期23卷 1745-1776页

作者： Jdey, Imen Hcini, Hazala Ltifi, Hela Sidi Bouzid Univ Kairouan Fac Sci & Technol Kairouan Tunisia Univ Sfax Natl Engn Sch Sfax ENIS ReGIM Lab Res Grp Intelligent Machines LR11ES48 Sfax Tunisia

Public health initiatives must be made using evidence-based decision-making to have the greatest impact. Machine learning algorithms are created to gather, store, process, and analyze data to provide knowledge and guide decisions. A crucial part of any surveillance system is image analysis. The communities of computer vision and machine learning have become curious about it as of late. This study uses a variety of machine learning, and image processing approaches to detect and forecast malarial illness. In our research, we discovered the potential of deep learning techniques as innovative tools with a broader applicability for malaria detection, which benefits physicians by assisting in the diagnosis of the condition. We investigate the common confinements of deep learning for computer frameworks and organizing, including the requirement for data preparation, preparation overhead, real-time execution, and explaining ability, and uncover future inquiries about bearings focusing on these constraints.

关键词： Malaria diagnosis machine learning deep learning convolutional neural network hybrid algorithms

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Efficient physics-based learned reconstruction methods for real-time 3D near-field MIMO radar imaging

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DIGITAL SIGNAL processing 2024年 144卷

作者： Manisali, Irfan Oral, Okyanus Oktem, Figen S. Middle East Tech Univ METU Dept Elect & Elect Engn TR-06800 Ankara Turkiye

Near-field multiple-input multiple-output (MIMO) radar imaging systems have recently gained significant attention. These systems generally reconstruct the three-dimensional (3D) complex-valued reflectivity distribution of the scene using sparse measurements. Consequently, imaging quality highly relies on the image reconstruction approach. Existing analytical reconstruction approaches suffer from either high computational cost or low image quality. In this paper, we develop novel non-iterative deep learning-based reconstruction methods for real-time near-field MIMO imaging. The goal is to achieve high image quality with low computational cost at compressive settings. The developed approaches have two stages. In the first approach, physics-based initial stage performs adjoint operation to back-project the measurements to the image-space, and deep neural network (DNN)-based second stage converts the 3D backprojected measurements to a magnitude-only reflectivity image. Since scene reflectivities often have random phase, DNN processes directly the magnitude of the adjoint result. As DNN, 3D U-Net is used to jointly exploit range and cross-range correlations. To comparatively evaluate the significance of exploiting physics in a learning-based approach, two additional approaches that replace the physics-based first stage with fully connected layers are also developed as purely learning based methods. The performance is also analyzed by changing the DNN architecture for the second stage to include complex-valued processing (instead of magnitude-only processing), 2D convolution kernels (instead of 3D), and ResNet architecture (instead of U-Net). Moreover, we develop a synthesizer to generate large-scale dataset for training the neural networks with 3D extended targets. We illustrate the performance through experimental data and extensive simulations. The results show the effectiveness of the developed physics based learned reconstruction approach compared to commonly used ap

关键词： Radar imaging Near-field microwave imaging deep learning 3D inverse imaging problems Sparse MIMO array

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