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YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems

Multimedia Tools and Applications 2025年第7期84卷 3809-3840页

作者： Berwo, Michael Abebe Fang, Yong Sarwar, Nadeem Mahmood, Jabar Aljohani, Mansourah Elhosseini, Mostafa School of Information and Engineering Chang’an University Shaanxi Xi’an710064 China Department of Computer Science Bahria University Lahore Campus Punjab Lahore54600 Pakistan Department of Information Technology Faculty of Computing and Information Technology University of Sialkot Punjab Sialkot51040 Pakistan College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt

In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Deep neural networks

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FRNet: A Full-Resolution Convolutional Neural Network for OCTA Vascular Segmentation 30

FRNet: A Full-Resolution Convolutional Neural Network for OC...

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30th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2024

作者： Wang, Liang Gao, Dongxu Gao, Hongwei Jiang, Guozhang Ju, Zhaojie Shenyang Ligong University School of Automation and Electrical Engineering Shenyang China University of Portsmouth School of Computing United Kingdom Wuhan University of Science and Technology Key Laboratory of Metallurgical Equipment and Control Technology Ministry of Education Wuhan China

ISBN: (纸本)9798350391916

Optical coherence tomography angiography (OCTA) is an advanced, non-invasive imaging technique. Automatic segmentation of vascular networks in retinal OCTA images is essential for the early diagnosis and progression assessment of various vision-related diseases. However, most existing methods for OCTA image vessel segmentation rely on encoder-decoder architectures, which typically involve a high number of parameters and result in slower inference speeds. In this paper, we introduce FRNet V2, an accurate and efficient neural network specifically designed for retinal vessel segmentation in OCTA images. This is achieved by integrating a modified recursive ConvNeXt V2 block and a recursive HAAM attention mechanism into a full-resolution convolutional network framework. We evaluate our proposed method on two large public datasets: ROSSA and OCTA-500. Experimental results demonstrate that our network achieves performance on par with other methods, while boasting significantly fewer parameters and faster inference speeds. This makes it highly suitable for practical industrial applications. © 2024 IEEE.

关键词： Optical coherence tomography

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An Intelligent Battery Management System for an Electric Vehicle Powered by Solar PV Array 59

An Intelligent Battery Management System for an Electric Veh...

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59th International Universities Power engineering Conference, UPEC 2024

作者： Challoob, Ali Falih Bin Rahmat, Nur Azzammudin Ramachandaramurthy, Vigna K. Saeed, Nagham Humaidi, Amjad Jaleel Institute of Power Engineering Universiti Tenaga Nasional Strategic Planning Centre Malaysia School of Computing and Engineering University of West London London United Kingdom University of Technology Control and Systems Eng. Dept. Baghdad Iraq

ISBN: (纸本)9798350379730

Implementing a Battery Management System (BMS) in Electric Vehicles (EVs) offers several benefits that enhance the efficiency, security, and durability of the vehicle's battery system. The BMS oversees and controls the performance of each cell in the battery pack, guaranteeing that they function within predetermined voltage and temperature thresholds to maintain safety. In this paper, an intelligent BMS for an EV model powered by a solar PV array was proposed using a fuzzy logic (FL) controller. The proposed method was applied to EVs consisting of four Lithium-ion batteries. The simulation results are achieved using MATLAB/Simulink software. The estimation of the state of charge (SOC) for each battery was used as input to the FL which controls the discharging/ charging mode for each battery via power semiconductor switches. Three different scenarios are used to verify the suggested method. A full control FL operation was obtained, and the batteries were protected successfully against overcharging and over-discharging. © 2024 IEEE.

关键词： MATLAB

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Safety-Critical Defensive Guidance of Autonomous Surface Vehicles for Domain Protection 1

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Kang, Tong Gu, Nan Wang, Dan Peng, Zhouhua Lv, Guanghao School of Marine Electrical Engineering Dalian Maritime University Dalian116026 China Dalian Key Laboratory of Swarm Control and Electrical Technology for Intelligent Ships Dalian116026 China School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia

ISBN: (数字)9789819733408

ISBN: (纸本)9789819733392

This paper addresses the safety-critical defensive guidance of autonomous surface vehicles (ASVs) concerning the interception of multiple attackers to protect a given domain within an obstacle environment. First, an optimal defensive matching method is proposed, which efficiently assigns multiple defensive ASVs to corresponding attackers. Then, an optimal collision-free defensive guidance law is developed to efficiently intercept an attacker in the presence of stochastic measurement noise. By using the proposed safety-critical defensive guidance method, the defensive ASVs are able to intercept the matched attackers with guaranteed safety. Through the stability analysis, the closed-loop system is practical stochastic input-to-state stability. Simulation results substantiate the effectiveness of the proposed safety-critical defensive guidance for the domain protection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Stochastic systems

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Energy Management Strategy for Fuel Cell Hybrid Emergency Power System based on Deep Reinforcement Learning 4

Energy Management Strategy for Fuel Cell Hybrid Emergency Po...

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4th International Symposium on AI, ISAI 2024

作者： Xi, Jingyi Hu, Xiaoyin Xiao, Duo Zhejiang University College of Control Science and Engineering Hangzhou China Hangzhou City University School of Information and Electrical Engineering Hangzhou China Hangzhou City University School of Computer and Computing Science Hangzhou China Hangzhou City University The Academy of Edge Intelligence Hangzhou China

ISBN: (纸本)9798350342482

Addressing the challenges in energy consumption and lifespan of fuel cell hybrid emergency power system, this paper proposes an energy management strategy based on deep reinforcement learning. The strategy aims to holistically balance objectives such as hydrogen consumption of the fuel cell, its lifespan, the stability of the lithium battery's state of charge (SOC), and its degradation. Firstly, we present the system architecture of the fuel cell hybrid emergency power system and establish models for hydrogen consumption and degradation of the fuel cell, as well as the SOC variations and degradation of the lithium battery, upon which a multi-objective reward function is constructed. Subsequently, we adopt the distributional soft actor-critic (DSAC) deep reinforcement learning algorithm, combined with a maximum entropy-based objective function to balance the exploration-exploitation trade-off and incorporated with a distributed value function estimation to alleviate the overestimation of Q-values. Simulation results demonstrate that the proposed strategy can effectively reduce the overall value loss, extend the system's lifespan, and showcase robust adaptability. © 2024 IEEE.

关键词： Deep reinforcement learning

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Biomedically Informed ECG Synthesis: Customizing Cardiac Cycle Phases with Diffusion Model

Biomedically Informed ECG Synthesis: Customizing Cardiac Cyc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Lin, Yuxin Ma, Jing Wang, Wei Wu, Zhihao Dong, Suyu Luo, Gongning Wang, Kuanquan Harbin Institute of Technology School of Computer Science and Technology Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Harbin Institute of Technology Faculty of Computing Harbin China

ISBN: (纸本)9798350386226

Cardiovascular diseases are a major global health challenge, with electrocardiography (ECG) being critical for diagnosis and monitoring. As artificial intelligence and automated ECG diagnostic technologies rapidly advance, the demand for large-scale ECG databases continues to grow. Generative ECG has become a mainstream method to enhance database size and diversity. However, existing methods typically generate ECG randomly or focus on limited physiological categories, lacking the ability to synthesize ECG with varying physiological features and cardiac cycles, which is crucial for various practical applications. In response to this need, we propose a novel approach introducing a diffusion model called DIFF-ECG to generate precisely customized ECG that accurately reflect diverse cardiac conditions. Segmentation-based quality assessments confirmed that the synthesized ECG accurately followed the specified cardiac cycle information, with our model significantly outperforming baseline diffusion and GAN-based methods. Therefore, our approach addresses the critical need for generating clinically relevant and customizable ECG, contributing significantly to the field of automated cardiac disease diagnosis. By enabling fine-tuning of cardiac cycle phases, our method significantly expands the application range of generative ECG, potentially improving the diagnostic accuracy for rare diseases and advancing personalized medicine. © 2024 IEEE.

关键词： Electrocardiography

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Synthesis of Decoherence-Free Modes in Linear Quantum Passive Systems via Robust Pole Placement

Synthesis of Decoherence-Free Modes in Linear Quantum Passiv...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Miao, Zibo Pan, Yu Gao, Qing School of Mechanical Engineering and Automation Adaptive Robotics Harbin Institute of Technology Guangdong Key Laboratory of Intelligent Morphing Mechanisms Shenzhen518055 China The Institute of Cyber-Systems and Control College of Control Science and Engineering Zhejiang University State Key Laboratory of Industrial Control Technology Hangzhou310027 China The School of Automation Science and Electrical Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665410205

In this paper we extend our previous research on coherent observer-based pole placement approach to study the synthesis of robust decoherence-free (DF) modes for linear quantum passive systems, which is aimed at preservation of quantum information. In particular, DF modes can be generated by placing the poles on the imaginary axis via a coherent feedback design scheme, and these modes can further be simultaneously made robust against perturbations to the system parameters by minimizing the condition number associated with imaginary poles. We develop explicit algebraic conditions for the existence of such a coherent quantum controller, with the corresponding deign procedure provided. Examples are given to illustrate the process of tuning the DF modes towards perfect robustness via the proposed pole placement technique. © 2024 IEEE.

关键词： Poles

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Synergistically Learning Class-specific Tokens for Multi-class Whole Slide Image Classification

Synergistically Learning Class-specific Tokens for Multi-cla...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Sun, Pengzhong Wang, Wei Li, Xiangyu Dong, Suyu Li, Shuo Wang, Kuanquan Luo, Gongning Harbin Institute of Technology Faculty of Computing Harbin China Harbin Institute of Technology Faculty of Computing Shenzhen China Northeast Forestry University College of Computer and Control Engineering Harbin China Case Western Reserve University Case School of Engineering Cleveland United States

ISBN: (纸本)9798350337488

The application of transformer architecture in analyzing whole slide images (WSIs) has become increasingly popular due to its remarkable ability to learn complex associations. Nevertheless, a significant drawback emerges in the multiclass analysis of WSIs. The majority of the transformer-based methods available currently rely primarily on a single, class-agnostic token. This approach might not ideally capture the subtleties of class-discriminative information. To address this challenge, we present an innovative approach tailored for multi-class WSI analysis that harnesses the power of class-specific tokens. Central to our method is a novel attention mechanism designed to foster a synergistic learning relationship between patch and class tokens, enhancing the granularity of information captured and ensuring a more comprehensive representation of the WSI. Complementing this, we introduce a dynamic class-centric training strategy designed to optimize token representation learning, ensuring each token is informatively aligned with its corresponding class. Through extensive experimentation on three challenging multi-class WSI analysis datasets, our method consistently demonstrates superior performance, underscoring its potential as a robust solution for multi-class WSI analysis tasks. © 2023 IEEE.

关键词： Multi-class whole slide image analysis Multiple instance learning Transformer

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Weakly supervised target detection based on spatial attention

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Visual Intelligence 2024年第1期2卷 1-11页

作者： Zhao, Wenqing Xu, Lijiao School of Control and Computer Engineering North China Electric Power University Hebei Baoding 071000 China Department of Hebei Key Laboratory of Knowledge Computing for Energy & Power North China Electric Power University Hebei Baoding 071000 China

Due to the lack of annotations in target bounding boxes, most methods for weakly supervised target detection transform the problem of object detection into a classification problem of candidate regions, making it easy for weakly supervised target detectors to locate significant and highly discriminative local areas of objects. We propose a weak monitoring method that combines attention and erasure mechanisms. The supervised target detection method uses attention maps to search for areas with higher discrimination within candidate regions, and then uses an erasure mechanism to erase the region, forcing the model to enhance its learning of features in areas with weaker discrimination. To improve the positioning ability of the detector, we cascade the weakly supervised target detection network and the fully supervised target detection network, and jointly train the weakly supervised target detection network and the fully supervised target detection network through multi-task learning. Based on the validation trials, the category mean average precision (mAP) and the correct localization (CorLoc) on the two datasets, i.e., VOC2007 and VOC2012, are 55.2% and 53.8%, respectively. In regard to the mAP and CorLoc, this approach significantly outperforms previous approaches, which creates opportunities for additional investigations into weakly supervised target identification algorithms. © The Author(s) 2024.

关键词： Erasure concept Multi instance learning Object detection Spatial attention Weakly supervised learning

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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