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real-time Classification of Vehicles Using Machine learning Algorithm on the Extensive Dataset

IEEE ACCESS 2024年 12卷 98338-98351页

作者： Pemila, M. Pongiannan, R. K. Narayanamoorthi, R. Sweelem, Emad A. Hendawi, Essam Abu El-Sebah, Mohamed I. SRM Inst Sci & Technol Dept Elect & Elect Engn Kattankulathur 603203 India SRM Inst Sci & Technol Sch Comp Dept Comp Technol Kattankulathur 603203 India Elect Res Inst Dept PV Cells Cairo 12622 Egypt Taif Univ Coll Engn Dept Elect Engn Taif 21944 Saudi Arabia Elect Res Inst Dept Power Elect & Energy Convers Cairo 12622 Egypt

Vehicle classification (VC) is a prominent research domain within image processing and machine learning (ML) for identifying vehicle volumes and traffic rule violations. In developed countries, nearly 40% of daily accidents are fatal, while in developing countries, the figure rises to 70%. Traditionally, vehicle detection and classification have been performed manually by experts, which is difficult, time-consuming, and prone to errors. Furthermore, incorrect detection and classification can result in hazardous situations. This highlights the need for more reliable techniques to identify and classify vehicles accurately and practically. In existing applications, numerous automated methods have been proposed. However, employing deep and machine learning algorithms on complex datasets of vehicle images has failed to achieve accuracy in various climate conditions and has been time-consuming. This paper presents an accurate, robust, real-time system to classify vehicles from onsite roads. The proposed system utilizes a random wavelet transform for pre-processing, edge and region-based segmentation for feature extraction, an embedded method for feature selection, and the XGBoost algorithm for VC. The proposed work classifies vehicles under complex weather, illumination, color, and occlusion conditions over 10 datasets, including a novel dataset named SRM2KTR, containing 75,436 vehicle images on an FPGA platform. The results show 98.81% accuracy, outperforming the state-of-the-art (98%). The system was demonstrated with four different classifiers, classifying images in 0.16 ns with an average accuracy of 97.79%. The system exhibits high accuracy, rapid identification time, and robustness in practical use.

关键词： Feature extraction Accuracy Task analysis Machine learning algorithms Reluctance motors image edge detection Classification algorithms Gradient methods Boosting Vehicle classification machine learning eXtreme gradient boost algorithm

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Deploying the model of improved heuristic-assisted adaptive SegUnet plus plus and multi-scale deep learning network for liver tumor segmentation and classification

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JOURNAL OF real-time image processing 2025年第1期22卷 1-20页

作者： Lakshmi, P. Sampurna Nagadevi, D. Suman, K. deepthi, Ragodaya Chikyal, Neetu Vallurupalli Nageswara Rao Vignana Jyothi Inst Eng Elect & Instrumentat Engn Hyderabad 500090 Telangana India Chaitanya Bharathi Inst Technol Dept ECE Hyderabad 500075 Telengana India Vasavi Coll Engn A Dept ECE Hyderabad 500031 Telangana India

Across worldwide, the major cause of death related to cancer disorder is liver tumor. Nowadays in medical industry, the liver cancer diagnosis model is a bottleneck since this disease incurs in the inner part of the body. To identify the disease, imaging techniques like Computed Tomography (CT) images are required. Hence, this paper develops an intelligent liver cancer segmentation and classification model employing diverse methodologies. At the starting stage, the raw images are fetched from the advanced benchmark data sources. The collected images undergo for segmentation. When compared to basic Unet model, it triggers the gradient vanishing issue that degrades the image resolution. Also, the parameter issue is the major hindrance to achieve the better classification process. Therefore, the Adaptive SegUnet++ (ASUnet++) is newly developed, where the essential parameters are optimally chosen by employing the Enhanced Lemurs Optimizer (ELO). The traditional model is vulnerable against training speed, gradient explosion and overfitting problem, which paves the way for misclassification and ends up with inaccurate results. Hence, this can be resolved using the residual connection in the deep learning classifier and multiscale concept. In the end, the segmented images are transferred to the Multiscale EfficientnetB7 with Residual Long Short-Term Memory (MEB7-RLSTM), which is used to provide the classified outcome as either the tumor disorder is incurred or not. The numerical findings of the model are represented in terms of 96.09 for accuracy, 93.48 for precision and 94.75 for f1-score, correspondingly. Hence, the final outcome exploits that it achieves the high true values that aid the practitioner to timely detect the disease and cure the patients efficiently.

关键词： Liver tumor segmentation and classification using CT images Adaptive Segunet plus plus Enhanced lemurs optimizer Multiscale Efficientnetb7 with residual long short-term memory

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HARNESSING deep learning FOR DISEASED AND FRESH ORANGE DETECTION: A COMPARATIVE ANALYSIS OF YOLO V8 AND RT-DETR 13

HARNESSING DEEP LEARNING FOR DISEASED AND FRESH ORANGE DETEC...

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13th International Conference on image processing Theory Tools and Applications

作者： Roy, Kyamelia Osmani, Mohammad Hatif Pal, Tapan Kumar Chaudhuri, Sheli Sinha Djemal, Khalifa Feiz, Amir Ali Siliguri Govt Polytech Dept ETCE Siliguri India Jadavpur Univ Dept ETCE Kolkata India Kanyapur Polytech Dept CST Asansol W Bengal India Univ Paris Saclay Univ Evry Val dEssonne Gif Sur Yvette France

ISBN: (纸本)9798331541859;9798331541842

Automation in horticulture with computer vision and deep learning revolutionizes the industry by enabling precise and efficient harvesting and disease detection. This technology enhances productivity and quality control, benefiting farmers and food manufacturers alike. The present work offers a comparative analysis of advanced deep learning detection models-YOLO V8, RT-DETR with ResNet and with EfficientNet backbones-focused on identifying diseased (canker) and healthy (fresh) oranges. A comprehensive evaluation of these three models was conducted to assess their performance. YOLO V8 emerged with superior results, showcasing its robustness and effectiveness for real-time detection. Amongst the 3models YOLO V8 attained the best accuracy value of 99.6%. The other performance metrics underscore the model's high accuracy and efficiency in identifying and classifying objects with minimal errors, affirming its suitability for real-time horticultural diagnostics. The comparative analysis highlights YOLO V8's superiority over RT-DETR models with ResNet and EfficientNet backbones, positioning it as a highly reliable choice for detecting healthy and diseased oranges. This work advances the field by proving the superior performance of YOLO V8 for this specific application and validating the performance with the other sophisticated deep learning architectures.

关键词： YOLO V8 RT-DETR (real time Detection Transformer) ResNet EfficientNet mAP (Mean Average Precision) Detection

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ADDITION: Detecting Adversarial Examples With image-Dependent Noise Reduction

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IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING 2024年第3期21卷 1139-1154页

作者： Wang, Yuchen Li, Xiaoguang Yang, Li Ma, Jianfeng Li, Hui Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Sch Cyber Engn State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Purdue Univ Dept Comp & Informat Technol W Lafayette IN 47907 USA Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China

Notwithstanding the tremendous success of deep neural networks in a range of realms, previous studies have shown that these learning models are exposed to an inherent hazard called adversarial example - images to which an elaborate perturbation is maliciously added could deceive a network, which entails the study of countermeasures urgently. However, existing solutions suffer from some weaknesses, e.g., parameters are usually determined empirically in some processing-based detection methods might result in a sub-optimal effect, and the directly performed processing on images might affect the classification of benign samples, leading to increment of false positive. In this paper, we propose a novel image-DepenDent noIse reducTION (ADDITION) model based on deep learning for adversarial detection. The ADDITION model can adaptively convert the adversarial perturbation in each image to approximate Gaussian noise by injecting image-dependent additional noise, then perform noise reduction to eliminate the adversarial perturbation, and finally detect adversarial examples by examining the classification inconsistency between the input image and its denoised version. The ADDITION model is trained end-to-end on benign samples without any prior knowledge of adversarial attacks, and thus avoid time-consuming task of generating adversarial examples in practical use. We generate more than 220,000 adversarial examples based on six attack algorithms for evaluation and present state-of-the-art comparisons on three real-word datasets. Extensive experiments demonstrate that our proposed method achieves improved performance in both detection accuracy rate and false positive rate.

关键词： Perturbation methods Noise reduction Gaussian noise Adaptation models Neural networks Face recognition Robustness Adversarial attack adversarial detection deep neural network noise reduction

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CT synthesis with deep learning for MR-only radiotherap planning: a review

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BIOMEDICAL ENGINEERING LETTERS 2024年第6期14卷 1259-1278页

作者： Roh, Junghyun Ryu, Dongmin Lee, Jimin Ulsan Natl Inst Sci & Technol Grad Sch Artificial Intelligence 50 Unist Gil Ulsan 44919 South Korea Seoul Natl Univ Program Biomed Radiat Sci 71 Ihwajang Gil Seoul 03087 South Korea Ulsan Natl Inst Sci & Technol Dept Nucl Engn 50 Unist Gil Ulsan 44919 South Korea Ulsan Natl Inst Sci & Technol Grad Sch Hlth Sci & Technol 50 Unist Gil Ulsan 44919 South Korea

MR-only radiotherapy planning is beneficial from the perspective of both time and safety since it uses synthetic CT for radiotherapy dose calculation instead of real CT scans. To elevate the accuracy of treatment planning and apply the results in practice, various methods have been adopted, among which deep learning models for image-to-image translation have shown good performance by retaining domain-invariant structures while changing domain-specific details. In this paper, we present an overview of diverse deep learning approaches to MR-to-CT synthesis, divided into four classes: convolutional neural networks, generative adversarial networks, transformer models, and diffusion models. By comparing each model and analyzing the general approaches applied to this task, the potential of these models and ways to improve the current methods can be can be evaluated.

关键词： MR-to-CT synthesis deep learning Computer vision MR-only radiotherapy planning

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Explainable deep neural network for in-plain defect detection during additive manufacturing

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RAPID PROTOTYPING JOURNAL 2024年第1期30卷 49-59页

作者： Kumar, deepak Liu, Yongxin Song, Houbing Namilae, Sirish Embry Riddle Aeronaut Univ Aerosp Engn Dept Daytona Beach FL 32114 USA Embry Riddle Aeronaut Univ Math Dept Daytona Beach FL USA Univ Maryland Dept Comp Sci Baltomore MD USA

PurposeThe purpose of this study is to develop a deep learning framework for additive manufacturing (AM), that can detect different defect types without being trained on specific defect data sets and can be applied for real-time process ***/methodology/approachThis study develops an explainable artificial intelligence (AI) framework, a zero-bias deep neural network (DNN) model for real-time defect detection during the AM process. In this method, the last dense layer of the DNN is replaced by two consecutive parts, a regular dense layer denoted (L1) for dimensional reduction, and a similarity matching layer (L2) for equal weight and non-biased cosine similarity matching. Grayscale images of 3D printed samples acquired during printing were used as the input to the zero-bias *** study demonstrates that the approach is capable of successfully detecting multiple types of defects such as cracks, stringing and warping with high accuracy without any prior training on defective data sets, with an accuracy of 99.5%.Practical implicationsOnce the model is set up, the computational time for anomaly detection is lower than the speed of image acquisition indicating the potential for real-time process control. It can also be used to minimize manual processing in AI-enabled ***/valueTo the best of the authors' knowledge, this is the first study to use zero-bias DNN, an explainable AI approach for defect detection in AM.

关键词： Additive manufacturing Explainable AI deep neural network Anomaly detection Defect detection

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A real-time recognition gait framework for personal authentication via image-based neural network: accelerated by feature reduction in time and frequency domains

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JOURNAL OF real-time image processing 2023年第5期20卷 92页

作者： Huang, Xuan Dong, Ran Wu, Bo Sato, Kiminori Ikuno, Soichiro Wang, Zijun Nishimura, Shoji Waseda Univ Metaverse Res Inst Tokorozawa Saitama 3591192 Japan Chukyo Univ Sch Engn Toyota Aichi 4700393 Japan Tokyo Univ Technol Sch Comp Sci Hachioji Tokyo 1920982 Japan Waseda Univ Fac Human Sci Tokorozawa Saitama 3591192 Japan

In recent years, personal authentication based on attitude estimation-gait recognition authentication has become a popular research topic because of its long-range, non-invasive, non-contact, high-precision, and other advantages. However, at present, most relevant research prefers to use the acquired original data directly for iteration and learning. As a result, it takes too long to learn relevant models in the use scenarios with complicated data and heavy human traffic, such as airports and railway stations, where real-time identification cannot be completed while maintaining accuracy, and thus a scheme to improve the learning and recognition speed is needed. Therefore, in this paper, we proposed an innovative real-time MediaPipe-based gait analysis framework and a new Composite Filter Feature Selection (CFFS) method via key nodes, angles, and lengths calculating. Then, based on the proposed method, we extract the aimed features as a new dataset and verified it by 1D-CNN neural network. Furthermore, we also applied Hilbert-Huang transform to investigate these extracted gait features in the frequency domain, improving the performance of our proposed framework to achieve real time under higher recognition accuracy. The experimental results show that the innovative gait recognition framework and data processing technology can reduce the gait feature data, speed up the process of gait recognition, and still maintain the original recognition accuracy. It can also be applied to various large, enclosed spaces with the huge human flow, which has played a role in improving the safety factor, saving labor costs, and accelerating economic consumption.

关键词： Computer vision real time deep learning Machine learning Gait features Acceleration Neural network Data analytics

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Intelligent Driving Task Scheduling Service in Vehicle-Edge Collaborative Networks Based on deep Reinforcement learning

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2024年第4期21卷 4357-4368页

作者： Wang, Nuanlai Pang, Shanchen Ji, Xiaofeng Wang, Min Qiao, Sibo Yu, Shihang China Univ Petr East China Coll Comp Sci & Technol Qingdao 266580 Peoples R China Yangzhou Univ Coll Informat Engn Yangzhou 225127 Jiangsu Peoples R China Tiangong Univ Coll Software Tianjin 300387 Peoples R China Tiangong Univ Sch Mech Engn Tianjin 300387 Peoples R China

With the evolution of 6G technology, mobile edge computing is rapidly advancing as a crucial application scenario. This research presents an innovative method for vehicle-edge task offloading decision-making, leveraging real-time data inputs such as channel conditions, image entropy, and detector confidence levels. We propose a collaborative task processing framework for vehicle-edge computing that effectively combines lightweight and heavyweight models to cater to varying demands, ensuring efficient task execution. Additionally, the study introduces a custom-designed reinforcement learning algorithm aimed explicitly at optimizing offloading scheduling. This algorithm boosts decision-making accuracy and efficiency and features a comprehensive reward system to achieve a balanced trade-off between detection performance and latency. The framework's efficacy is thoroughly evaluated in complex driving scenarios using the SODA10M dataset. Our results indicate the framework's capability to achieve convergence, enhance precision, ensure stability, and maintain a lightweight operation, emphasizing its suitability for real-world implementation. This work provides practical and efficient strategies for intelligent driving task scheduling to meet the requirements of contemporary dynamic environments.

关键词： Task analysis Computational modeling Processor scheduling Collaboration 6G mobile communication Servers Optimization 6G Internet of Vehicles task offloading edge computing collaborative computing deep reinforcement learning

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deep Reinforcement learning for Efficient IoT Data Compression in Smart Railroad Management

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IEEE INTERNET OF THINGS JOURNAL 2024年第15期11卷 25494-25504页

作者： Chen, Xuan Yu, Qixuan Dai, Shuhong Sun, Pengfei Tang, Haichuan Cheng, Long Zhejiang Ind Polytech Coll Shaoxing 312068 Zhejiang Peoples R China North China Elect Power Univ Sch Control & Comp Engn Beijing 102200 Peoples R China Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China AI Lab CRRC Acad Beijing 100070 Peoples R China

Modern smart railroad management relies heavily on the Internet of Things (IoT)-enabled sensors to monitor train performance, resulting in the generation of extensive data streams. The ensuing data influx poses critical challenges in storage, processing, and transmission. This article presents a novel data compression method specifically designed for IoT-generated data within railroad management scenarios. Leveraging deep reinforcement learning (DRL), our approach intelligently compresses data from onboard IoT sensors. This not only streamlines data streams, ensuring essential information is retained and redundant data is pruned but also alleviates strain on resource-limited devices such as edge servers tasked with complex computations or aggregating large data sets for long-term trend analysis. Experimental results demonstrate that our approach can outperform current baselines with achieving an enhancement of more than 18% on compression rate in real-time configurations. This signifies a transformative solution for handling IoT-induced big data in contemporary railroad management systems.

关键词： Data compression Internet of Things real-time systems Servers Task analysis Market research image coding deep reinforcement learning (DRL) edge computing Internet of Things (IoT) smart railroad management

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Attention-based transformer models for image captioning across languages: An in-depth survey and evaluation

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COMPUTER SCIENCE REVIEW 2025年 58卷

作者： Albadarneh, Israa A. Hammo, Bassam H. Al-Kadi, Omar S. Univ Jordan Amman 11941 Jordan Princess Sumaya Univ Technol Amman 11941 Jordan

image captioning involves generating textual descriptions from input images, bridging the gap between computer vision and natural language processing. Recent advancements in transformer-based models have significantly improved caption generation by leveraging attention mechanisms for better scene understanding. While various surveys have explored deep learning-based approaches for image captioning, few have comprehensively analyzed attention-based transformer models across multiple languages. This survey reviews attention-based image captioning models, categorizing them into transformer-based, deep learning-based, and hybrid approaches. It explores benchmark datasets, discusses evaluation metrics such as BLEU, METEOR, CIDEr, and ROUGE, and highlights challenges in multilingual captioning. Additionally, this paper identifies key limitations in current models, including semantic inconsistencies, data scarcity in non-English languages, and limitations in reasoning ability. Finally, we outline future research directions, such as multimodal learning, real-time applications in AI-powered assistants, healthcare, and forensic analysis. This survey serves as a comprehensive reference for researchers aiming to advance the field of attention-based image captioning.

关键词： image captioning Transformer Attention mechanism Convolutional neural network Computer vision Natural language processing

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