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检索条件"主题词=detection Algorithms"

共 7428 条记录，以下是691-700 订阅

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YOLOv8 Traffic Sign detection Incorporating Attention Mechanisms

YOLOv8 Traffic Sign Detection Incorporating Attention Mechan...

引用

Algorithm, Image Processing and Machine Vision (AIPMV), International Conference on

作者： Wenlin Wang Zongyi Xing Yingshun Liu Yuzhe Qiu Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350390254

ISBN: (纸本)9798350390261

Aiming at the problems of existing traffic sign detection algorithms limited by light, occlusion and low detection accuracy, a YOLOv8 traffic sign detection algorithm incorporating an attention mechanism is proposed. By introducing the attention mechanism, the model can be allowed to automatically select the region of interest, thus reducing the unnecessary computational overhead and decreasing the influence of interference factors, thus improving the speed and accuracy of model detection. The experimental results show that the improved algorithm improves the accuracy and average accuracy by 2.9% and 2% respectively compared with the original algorithm, and the frame rate reaches 112FPS, which meets the requirements of real-time detection.

关键词： Training Accuracy Attention mechanisms Computational modeling Machine vision Object detection Interference Real-time systems Robustness detection algorithms

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detection of Rice Sheath Blight Leaf Based on Improved YOLOV8 Algorithm

Detection of Rice Sheath Blight Leaf Based on Improved YOLOV...

引用

2024 International Symposium on Computer Science and Educational Technology (ISCSET)

作者： Yuze Zhang Yuxuan Zhou Hui Yang Liming Jian Fangyu Shi Aili Wang Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Harbin China Heilongjiang Provincial Metrological Verification and Testing Institute Harbin China

ISBN: (数字)9798350390803

ISBN: (纸本)9798350390810

Traditional YOLOv8 algorithm fails to achieve the desired detection accuracy in object detection. Therefore, this paper proposes an improved YOLOv8 algorithm by introducing the BiLevel Routing Attention mechanism and the Wasserstein loss function, which significantly improves the accuracy of the YOLOv8 algorithm. Results show that compared to YOLOv8, the proposed system exhibits improved overall recognition accuracy for the detection of withered rice, as evidenced by various evaluation metrics. The overall recognition accuracy reaches 98.7%.

关键词： Measurement Computer science Accuracy Attention mechanisms Object detection Educational technology Routing detection algorithms Diseases

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A Real-time detection Algorithm Based On Improved Nanodet For Shaft Parts

A Real-time Detection Algorithm Based On Improved Nanodet Fo...

引用

International Conference on Intelligent Human-Machine Systems and Cybernetics, IHMSC

作者： Yuhan Wang Yifei Wu School of Automation Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331540241

ISBN: (纸本)9798331540258

Aiming at the problems of excessive parameter amount, slow speed and low accuracy of traditional mechanical parts target detection model, a real-time shaft parts detection algorithm based on improved Nanodet is proposed. Firstly, we introduce two attention mechanism modules with no or little parameter into the original backbone network, which improve the detection effect of small targets while hardly increasing the parameters of the model. Secondly, we adopt GELU instead of the original activation function to improve the convergence speed and shorten the detection time. Finally, we adopt Soft-NMS to reduce the probability of missed and false detection in the case of target aggregation. Through experiments on the simulated shaft part dataset, mean average precision of the improved Nanodet reaches 84%, which is 12% higher than the original model, and the detection speed reaches 91 frames per second, which is 21% higher. Compared with other mainstream lightweight detection models, it also behaves better in various aspects such as speed, accuracy and model volume. The experiment results show that the improved model blances high efficiency, high accuracy and lightweight, which can accurately and quickly identify shaft part categories. Therefore, it is easy to be used on small mobile devices and has good deployment prospects.

关键词： Shafts Deep learning Solid modeling Accuracy Attention mechanisms Human-machine systems Object detection Real-time systems Mobile handsets detection algorithms

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Research on Policy Conflict detection Technology Based on Interdomain Interconnection

Research on Policy Conflict Detection Technology Based on In...

引用

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Daqiu Wang Liangyu Dong Ziyi Wang Peijun Chen Weiruo Pu Yixiang Li Nuclear Power Institute of China Chengdu China China Industrial Control Systems Cyber Emergency Response Team Beijing China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

Security policies can effectively maintain the order of interconnection between domains and secure access. However, with the expansion of network scale and the increasing number of policies, the problem of policy conflict occurs from time to time. In order to achieve efficient detection of conflicts between security policies, this paper proposes a fusion index conflict detection algorithm based on B+-tree and bitmap, which further reduces the scope of policy comparison by dividing the dimensions of policy attributes and constructing a fusion index, so as to effectively improve the detection efficiency. Experiments show that this conflict detection algorithm can shorten the detection time consumption by up to 23% compared with the conflict detection algorithm with a single B+ tree index without losing the detection accuracy.

关键词： Production Libraries Indexes Security detection algorithms Computational intelligence

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CoCoIDS: A Collaborative Intrusion detection System for IoT based on Co-evolution

CoCoIDS: A Collaborative Intrusion Detection System for IoT ...

引用

International Conference on Internet of Things: Systems, Management and Security

作者： Ali Deveci Selim Yılmaz Sevil Sen WISE Lab. Hacettepe University Ankara Turkey Department of Software Engineering Mugla Sitki Kocman University Mugla Turkey

ISBN: (数字)9798350366501

ISBN: (纸本)9798350366518

The vulnerability of the Routing Protocol for Low-Power and Lossy Networks (RPL) to insider attackers highlights the importance of detecting and responding to malicious attempts to ensure uninterrupted protocol operation. Researchers are generally developing collaborative Intrusion detection Systems (IDS) to increase detection performance, yet achieving a balance between effectiveness and efficiency remains challenging, often leading to compromises. Here, we introduce CoCoIDS, a Collaborative Co-evolution Based Intrusion detection System for IoT. Unlike existing approaches, our solution prioritizes both effectiveness and efficiency, including communication cost and resource consumption in collaborative nodes. Through a cooperative co-evolution strategy based on multi-objective optimization, our approach simultaneously optimizes the detection algorithm and collaborator node selection. Minimizing the number of collaborator nodes is especially important in a lossy environment, where communication disruptions can block collaborative efforts. The results show a balanced trade-off between accuracy and the number of collaborator nodes. Additionally, the study thoroughly analyzes selected collaborators based on their locations and targeted attack types.

关键词： Accuracy Costs Correlation Collaboration Intrusion detection Routing protocols Internet Security detection algorithms Optimization

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A Ginkgo detection Algorithm in Complex Environments Based on Improved YOLOv7

A Ginkgo Detection Algorithm in Complex Environments Based o...

引用

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Chi Ma Qiang Guo School of Computer Science and Engineering Huizhou University Huizhou China School of Computer Science and Software Engineering University of Science and Technology LiaoNing Anshan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

detections of Ginkgoes are prerequisites for later counting and harvesting. Due to the uneven distribution of samples, the detection speed and accuracy of existing algorithms cannot adapt to the impact of complex environments. Therefore, an enhanced model YOLOv7-DC based on YOLOv7 is proposed in this paper, which redesigns the detection network and introduces a new feature fusion method. DCNv2 is embedded in the efficient layer aggregation network (ELAN), while PConv is utilized instead of conventional convolution to reduce the parameter impact of DCNv2. Moreover, the attention mechanism CBAM is introduced during training to enhance spatial and channel information, and the ConvMixer architecture is employed to capture spatial and channel relationships within the features, which are transmitted to the detection head through attention mechanism, improving the model's detection accuracy for each specific classification sample. Experimental results show that our YOLOv7-DC achieves both excellent detection speed and recognition rate in various classification tasks. The improved model's average detection accuracy is increased by 6.2% compared to previous algorithms, and the model parameters are reduced by 13%. It is proved that YOLOv7-DC is more suitable for scenarios with imbalanced samples and complex environments.

关键词： Training Accuracy Attention mechanisms Convolution Signal processing algorithms Lighting Medical services Feature extraction Environmental factors detection algorithms

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Research on an Intelligent Obstacle detection Algorithm for High Voltage Lines Based on Improved YoLo Algorithm

Research on an Intelligent Obstacle Detection Algorithm for ...

引用

Power Electronics, Computer Applications (ICPECA), IEEE International Conference on

作者： Yanghong Qin School of Artificial Intelligence Chongqing Three Gorges Vocational College Chongqing China

ISBN: (数字)9798331533694

ISBN: (纸本)9798331533700

In this paper, an intelligent obstacle detection method based on improved YOLO (You Only Look Once) algorithm is proposed to improve the efficiency and safety of automated inspection of high-voltage transmission line corridors in power system. The operating environment of high-voltage lines is complex and changeable, and the existence of obstacles such as kites, nesting birds and hanging foreign objects poses a major threat to the stability and safety of the power system. Traditional manual inspection is not only inefficient, but also faces many security risks. Therefore, it is particularly important to develop an intelligent detection algorithm that can identify and locate obstacles in high voltage lines in real time and accurately. This study first analyzes the limitations of the original YOLO algorithm in the high-voltage line obstacle detection scenario, including the lack of precision for small target detection and the poor robustness against complex background. In order to solve these problems, we propose the following improvement measures: First, the Feature Pyramid Network (FPN) structure is introduced to enhance the detection ability of multi-scale obstacles; The second is to use the deep residual learning framework to solve the problem of gradient disappearance in the deep network training process, and further improve the detection accuracy of the model. The experimental results show that the improved YOLO algorithm has achieved significant improvement in the obstacle detection task of high-voltage lines, and has obvious advantages in Average Precision (AP) index compared with other classical target detection algorithms.

关键词： YOLO Accuracy High-voltage techniques Power system stability Inspection Feature extraction Robustness Real-time systems Safety detection algorithms

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Traffic Sign detection Algorithm Based on Improved YOLOv7

Traffic Sign Detection Algorithm Based on Improved YOLOv7

引用

Computer Engineering and Intelligent Communications (ISCEIC), International Symposium on

作者： Haodong Wang Xindong Zhang College of Electronic Science and Engineering Jilin University Changchun China

ISBN: (数字)9798331518677

ISBN: (纸本)9798331518684

With the development of autonomous driving, people have more requirements for traffic sign detection technology, and it is very important to accurately identify the traffic signs roadside for people's safety. In view of the problem of low traffic sign detection accuracy, a traffic sign detection algorithm based on YOLOv7 is proposed. In this paper, Efficient Channel Attention (ECA) is introduced, making the YOLOv7 pay more attention to effective features. The experimental results show that compared with the original YOLOv7 algorithm, the improved YOLOv7 mAP@0.5 was 77.46%, which increased by 3.86%.

关键词： Accuracy Safety detection algorithms Autonomous vehicles

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Reputation-Based Classification for Malicious User detection in Cognitive Radio Networks

Reputation-Based Classification for Malicious User Detection...

引用

International Communication Systems and Networks and Workshops, COMSNETS

作者： Ashutosh Nath Tiwari Ankit Chouhan Ashok Parmar Kamal Captain Sardar Vallabhbhai National Institute of Technology Surat India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Cognitive radio technology has advanced beyond traditional radio systems to efficiently utilize underutilized spectrum. In cognitive radio, cooperative secondary users (CSUs) engage in cooperative spectrum sensing (CSS) to access available frequency bands, ensuring no interference with primary users (PUs). However, the performance of cognitive radio networks (CRNs) is significantly impacted by security threats such as malicious users (MUs), who mimic genuine CSUs and transmit false information to the fusion center (FC), degrading the system’s effectiveness. To address this challenge, we propose a reputation score based algorithm to distinguish between genuine CSUs and MUs. The reputation score serves as a classifier, effectively separating trustworthy CSUs from malicious ones. Extensive simulations are conducted to demonstrate the effectiveness of this approach.

关键词： Interference Robustness Classification algorithms Sensors Cognitive radio Security Fake news detection algorithms Radiofrequency interference Radio spectrum management

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Optimization of Financial Fraud detection Algorithm by Combining Variational Autoencoder with Generative Adversarial Networks

Optimization of Financial Fraud Detection Algorithm by Combi...

引用

Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： Yu Liu Audit & Assurance Deloitte & Touche LLP Detroit MI United States

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

This article delves into the field of financial fraud detection and proposes innovative solutions based on big data, machine learning, and deep learning technologies to address the challenges posed by the complexity and technology driven new features of modern financial fraud that traditional credit evaluation methods face. The latest progress in financial fraud risk, detection technology, and machine learning applications was systematically reviewed through literature research, and the shortcomings of existing research were identified. To solve the problem of sample imbalance, this paper innovatively designs AE-G and VAE-G models, which use normal transaction samples to train and generate fraudulent samples, achieving efficient binary classification learning and effectively alleviating the problem of data imbalance. In empirical analysis, the excellent performance of AE-G and VAE-G models in fraud detection was verified using the credit card transaction dataset jointly developed by the Free University of Brussels and Worldline. In particular, the VAE-G model performed well in AUC, recall rate, and F1 score, and significantly improved the recognition ability of minority fraud transactions. This article not only provides new ideas for the field of financial fraud detection, but also provides technical support for practical applications. At the same time, it points out the limitations of dataset diversity and the authenticity of generated fraud samples, which provides direction for future research and is of great significance for promoting the development of financial technology in the field of anti fraud.

关键词： Performance evaluation Fintech Face recognition Diversity reception Finance Generative adversarial networks Feature extraction Fraud detection algorithms Optimization

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