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检索条件"主题词=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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Improved YOLOv8 Factory Safety Wearable detection Algorithm

Improved YOLOv8 Factory Safety Wearable Detection Algorithm

引用

Cognitive Computing and Complex Data (ICCD), International Conference on the

作者： Wenhao Wang Buying Fu Yanlun Wu Zhenyang Yu Chenjin Sun Xin Chen Faculty of Computer Engineering Huaiyin Institue of Technology Huaian China

ISBN: (数字)9798350352894

ISBN: (纸本)9798350352900

In industrial scenarios, the YOLOv8 algorithm is difficult to extract key features due to the complexity of background information in images such as helmets, safety vests, and gloves, and is prone to problems such as missed detection and false detection. The C2f_MSCA structure is constructed to replace the C2f module in the Backbone network of YOLOv8 to improve the key feature extraction ability of the Backbone network. The visual information contained in the small target objects in the dataset is very limited, it is difficult to capture its features, and it is easy to be covered by complex backgrounds. A GS_Head detection branch is added to the Head part to enhance the learning ability of small targets. The receptive field attention convolution is used in the neck, which reduces background interference by adjusting the response of the feature map in the dataset and ignoring the region unrelated to the target. The experimental results show that the proposed algorithm improves the mean average precision (mAP) by 1.2 compared with the original YOLOv8 model, which effectively improves the performance of the model.

关键词： Visualization Convolution Object detection Feature extraction Production facilities Robustness Safety Neck detection algorithms Kernel

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UAVLite-YOLOv8: UAV Small Target Lightweight detection Algorithm

UAVLite-YOLOv8: UAV Small Target Lightweight Detection Algor...

引用

Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Huan Liu Yusen Nie Xiaodong Cheng School of Electronic Information Engineering Inner Mongolia University Hohhot China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Aiming at the problems of UAVs in target detection, such as small target size, dense target, difficulty in feature extraction, and the excessive amount of parameters and high computation when the YOLO algorithm is applied to actual deployment, a UAV small target lightweight detection algorithm, UAVLite-YOLOv8, which is based on the improvement of YOLOv8 algorithm, is proposed. Dilation-wise Reparam Residual (DWRR) is firstly used to improve the C2f layer in the backbone, which improves the accuracy of the model while reducing the resource consumption. Global and local feature fusion (GLFF) is then used in the neck to improve the accuracy of the algorithm without significantly affecting the number of network parameters and computational effort. Finally, the detail fusion shared convolution lightweight detection (DFSCLD) head is proposed to replace the original algorithm detection head, which reduces the computational burden while maintaining the detection performance and makes the algorithm more suitable for real-time application scenarios. By conducting comparative experiments of different algorithms on the VisDrone2019 dataset, the results show that the UAVLite- YOLOv8 algorithm significantly outperforms other target detection algorithms. Compared with YOLOv8, the mAP@0.5 of UAVLite-YOLOv8 grows from 31.4% to 32.2%, the amount of parameters is reduced by 25.6%, and the amount of computation is reduced by 17.3%. In this way, it proves that the algorithm can better meet the needs of UAV small target lightweight detection.

关键词： YOLO Technological innovation Accuracy Head Quantization (signal) Object detection Autonomous aerial vehicles Real-time systems Reliability detection algorithms

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A Step-type Obstacle detection Method based on 3D Point Cloud Map

A Step-type Obstacle Detection Method based on 3D Point Clou...

引用

International Conference on Electronic Engineering and Informatics (EEI)

作者： Fanghong You Pengcheng Wang Rui Zhou Yihan Zhang Jian Zhang Tao Zhao College of Electrical Engineering Sichuan University Chengdu China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Addressing the challenge of precise detection of traversable obstacles resembling steps in 3D point cloud map generated by LiDAR, this paper proposes a detection algorithm based on Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and geometric constraints. The proposed method achieves accurate detection of step-type traversable obstacles in 3D point cloud map. Firstly, the 3D point cloud map is filtered to remove outliers and extract regions of interest. Then, the Random Sample Consensus (RANSAC) plane fitting algorithm is employed to detect and remove ground points for eliminating clustering interference. Subsequently, the DBSCAN algorithm is applied for density clustering of the point cloud map without ground points. Finally, geometric constraints resembling step-type traversable obstacles are applied to the clustering results to select the optimal detection targets. Experimental results demonstrate that the proposed detection algorithm can accurately detect various step-type traversable obstacles in indoor 3D point cloud maps, achieving desirable outcomes.

关键词： Point cloud compression Three-dimensional displays Accuracy Laser radar Noise Neural networks Clustering algorithms Robustness Labeling detection algorithms

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R-Wave detection Algorithm Based on Central Difference and Adaptive Multi-Threshold

R-Wave Detection Algorithm Based on Central Difference and A...

引用

Computer Information Science and Application Technology (CISAT), International Conference on

作者： Ke Xu Huibin Wang Sikai Guo Wenping Chen College of Information Science and Engineering Hohai University HHU Nanjing China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

As a non-invasive method for diagnosing heart diseases, electrocardiography (ECG) relies on R-wave detection as a foundational step for other ECG waveform analysis. To further meet the demands for accuracy and real-time detection of R-waves in automatic ECG analysis, this paper proposes a low-complexity, fast, and reliable real-time R-wave detection algorithm. An R-wave detection method based on central difference and adaptive multi-threshold is designed. First, this paper proposes the use of central difference for R-wave enhancement, effectively improving the detection capability for low amplitude and low slope R-waves. Secondly, an adaptive multi-threshold strategy is proposed, which adaptively adjusts the current detection threshold based on the detected R-wave amplitude and RR interval, making it suitable for R-wave detection in cases of sudden changes in RR interval and R-wave amplitude. The proposed algorithm’s performance was evaluated using ECG signals from the MIT-BIH database, achieving an overall sensitivity, positive predictive value, and detection error rate of 99.73%, 99.82%, and 0.46%, respectively. The processing time for a 30-minute ECG recording is approximately 0.52 seconds. The results indicate that the algorithm is accurate, efficient, and suitable for real-time automatic ECG analysis.

关键词： Accuracy Sensitivity Machine learning algorithms Electrocardiography Prediction algorithms Approximation algorithms Real-time systems Recording Reliability detection algorithms

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detection of False Data Injection Attacks in New Energy Systems Based on Supervised Learning

Detection of False Data Injection Attacks in New Energy Syst...

引用

Internet of Things, Automation and Artificial Intelligence (IoTAAI), International Conference on

作者： Tong Li Lei Wang Jian Chen Yang Liu Hongwei Zhao Hongbi Geng State Grid Liaoning Electric Power Supply Co. Ltd. Shenyang China State Grid Liaoning Electric Power Research Institute Shenyang China

ISBN: (数字)9798350386974

ISBN: (纸本)9798350386981

In recent years, with the rapid development of China’s new energy industry, and to the monitoring and data acquisition (SCADA) system brings a huge network security risks. False Data Injection Attack (FDIA) is a serious threat to the secure and stable operation of smart grids. In this paper, an FDIA detection algorithm that integrates unsupervised and supervised learning is proposed to address the problems of scarce labelled data and extreme imbalance between normal and attack samples in FDIA detection. Compared with other FDIA detection algorithms, the proposed method enhances the stability of the FDIA detection model in the case of scarce labelled samples and unbalanced data, improves the recognition accuracy of FDIA and reduces the false alarm rate.

关键词： Accuracy Supervised learning SCADA systems Power system stability Network security Stability analysis Smart grids detection algorithms Thermal stability Monitoring

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Research on the detection Algorithm for Abnormal Crowd Behaviors Based on an Enhanced SlowFast Model

Research on the Detection Algorithm for Abnormal Crowd Behav...

引用

International Conference on Computers, Communications, and Systems (ICCCS)

作者： Yueping Peng Hexiang Hao Tongtong Zhou Baixuan Han Wenji Yin Information Engineering Collage Engineering University of PAP Xian China Engineering University of PAP Xian China

ISBN: (数字)9798350350210

ISBN: (纸本)9798350350227

Aiming at the issues of inaccurate detection of behavioral classification in crowd abnormal behaviors detection algorithms and large model computation capacity, a crowd abnormal behaviors detection algorithm based on the improved SlowFast model is proposed. By changing the convolutional structure of the network to a Depthwise-separable convolution, adding a hybrid domain attention mechanism, and inserting Dropout to inhibit the overfitting phenomenon, we can enhance the network's precision of detection and classification of behaviors while ensuring the computational capacity. Experiments show that the method in this paper can detect and identify five types of behaviors in public places: normal, scattered, speeding up in the same direction, sudden gathering, and group fighting. Compared with the original SlowFast model and other algorithms, the precision is improved, and the number of detected frames per second can be up to 40.5 fps, which meets the real-time requirements, providing a new idea for the determination and classification of abnormal behaviors of crowds.

关键词： Attention mechanisms Convolution Communication systems Computational modeling Real-time systems Classification algorithms detection algorithms

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Pedestrian detection Algorithm Based on Multi-Scale Fusion and Dual-Backbone

Pedestrian Detection Algorithm Based on Multi-Scale Fusion a...

引用

Image Processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Deshuai Bao Wenzhong Zhu School of Computer Science & Engineering Sichuan University of Science & Engineering Yibin China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Addressing the limitations of visible light singlemodal target detection algorithms, which are affected by external factors like illumination, and infrared single-modal algorithms, which lack complete target information, we propose a pedestrian detection algorithm based on dual-backbone and multi-modal multi-scale fusion, building upon *** network employs a dual-backbone structure derived from the YOLOv8n lightweight network. An SMMF(Scale-Multiple Multimodal Feature Fusion) module is designed to fuse features from both modalities at multiple scales. The GhostConv module is introduced to enrich features and reduce computational cost. Additionally, the SBAp2 is integrated into the network neck, enhancing the fusion of deep and shallow features and leveraging both deep semantic information and shallow image location information. The ShapeIoU is adopted as the loss function, replacing the original bounding box regression loss, to incorporate the shape and scale of the bounding box into the loss calculation, thereby improving target detection accuracy. Experimental results indicate that the improved algorithm achieves an AP0.5 of 78.5% and an FPS of 156.3. Compared to the original visible light single-modal and infrared single-modal YOLOv8n algorithms, the missed detection rates were reduced by 30.1% and 13.4%, respectively, while the AP0.5 is increased by 21.2% and 8.2%, demonstrating its superiority over the single-modal YOLOv8n detection algorithm.

关键词： Pedestrians Accuracy Shape Semantics Object detection Reconnaissance Real-time systems Neck detection algorithms Standards

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