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检索条件"主题词=detection Algorithms"

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A lightweight small-object detection algorithm for remote sensing images based on 4SDC-YOLOv8

A lightweight small-object detection algorithm for remote se...

引用

Computer Applications and Information Technology (ISCAIT), International Symposium on

作者： Hanqing Ding Lichao Mo Shengfa Yue Jiaxue Li School of Electronics and Information Zhengzhou University of Light Industry Zhengzhou China School of Computer Science and Technology Zhengzhou University of Light Industry Zhengzhou China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

In the field of object detection, remote sensing image detection has become a critical research focus. However, existing small-object detection algorithms for remote sensing images often fail to strike an effective balance between detection accuracy and computational complexity, thereby limiting their application on mobile devices. To address this issue, this paper proposes a lightweight small-object detection algorithm for remote sensing images, named 4SDC-YOLOv8, built upon YOLOv8. Firstly, we incorporate the SimSPPF spatial pyramid pooling structure, using pooling kernels of different sizes across multiple scales to improve the detector’s performance. Secondly, we design a lightweight neck network that leverages the dynamic upsampling operator DySample and the partial convolution Partial Conv to introduce the new CSPPC architecture, reducing redundant computations and enhancing both real-time performance and model compactness. Finally, we add a dedicated small-object detection layer, enabling the feature map to capture rich semantic and spatial information, with four detection heads supporting multi-scale small-object detection and improving detection capability for small targets. Experimental results on the DIOR dataset show that, compared with YOLOv8, 4SDC-YOLOv8 significantly reduces model size and computational load by 24.6% and 24%, respectively, achieves an accuracy of up to 90%, and maintains a respectable real-time performance at 145 FPS. Confirm that 4SDC-YOLOv8 effectively balances detection accuracy and computational complexity, thereby offering a promising reference for implementing small-object remote sensing detection on mobile devices.

关键词： Performance evaluation YOLO Accuracy Computational modeling Semantics Real-time systems Mobile handsets detection algorithms Computational complexity Remote sensing

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Anomaly detection Algorithm for Multivariate Complex Data Based on Deep Neural Network

Anomaly Detection Algorithm for Multivariate Complex Data Ba...

引用

Artificial Intelligence, Internet and Digital Economy (ICAID), International Conference on

作者： Guangzhi Deng Haikun Wu Wen He Sheng Ding Qingqing Pan Zekun Ma State Grid Yinchuan Electric Power Supply Company Yinchuan Ningxia

ISBN: (数字)9798331510664

ISBN: (纸本)9798331510671

The conventional data anomaly detection structure is mostly single target execution, and the detection efficiency is relatively low, resulting in an increase in the final false detection rate. Therefore, the design and practical verification of the anomaly detection algorithm for multivariate complex data based on deep neural network are proposed. At present, the data anomaly detection feature extraction is carried out first, and the multi-target method is used to improve the detection efficiency and build a multi-target anomaly monitoring and calculation structure. Based on this, the deep neural network data anomaly detection and calculation model is designed, and the adaptive cascade scheduling method is used to achieve anomaly detection. The test results show that compared with YOLOv7 target detection algorithm and multiple data enhancement strategy MODIS data segmentation optimization and typical related forest algorithm. The final false detection rate of the multi complex data anomaly detection algorithm of the deep neural network designed this time is relatively low, which indicates that the detection algorithm designed for abnormal data is more efficient, the detection range is identifiable, targeted and has practical application value with the help and support of the deep neural network technology.

关键词： Representation learning Noise Artificial neural networks Object detection Feature extraction Robustness detection algorithms Anomaly detection Optimization Periodic structures

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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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Non-coherent Signal detection Algorithm Based on Molecular Communication and Its Experimental Verification

Non-coherent Signal Detection Algorithm Based on Molecular C...

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World Congress on Computing and Communication Technologies (WCCCT)

作者： Leifu Ren Yitao Hu Xiaoyi Bian Chuwen Lan Jiaxing Wang Guoan Zhao Zhimin Zheng Chunfeng Cui Zehua Gao China Mobile Information Technology Co. Ltd Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Beijing China North China Institute of Aerospace Engineering Hebei China China Mobile Research Institute Beijing China

ISBN: (数字)9798331512620

ISBN: (纸本)9798331512637

Demodulation at the receiver is a pivotal process in molecular communication (MC), involving the identification and transformation of transmitted signals to retrieve the original information from the sender. Due to the unique properties of MC, traditional demodulation techniques from electromagnetic communication cannot be directly applied. The demodulation process in MC must account for complex factors such as molecular diffusion, reaction kinetics, and environmental influences, which can affect the receiver's ability to recognize and process signals. Most existing signal detection methods rely on an unbounded model, which does not align with real-world communication scenarios, and are unsuitable for bounded environments. Addressing these issues, this paper proposes a non-coherent signal detection method suitable for bounded models, using pre-smoothing of data to set thresholds for current time slots. Through experimental simulations of the communication process using the molecular communication simulator developed by China Mobile, we analyze the channel capacity and bit error rate (BER). The results demonstrate that our approach significantly reduces the BER and enhances the reliability of MC in bounded environments.

关键词： Analytical models Computational modeling Bit error rate Molecular communication Receivers Reliability Channel models detection algorithms Signal detection Demodulation

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Novel Abnormal Data Mining and detection Algorithm for Intelligent Systems

Novel Abnormal Data Mining and Detection Algorithm for Intel...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Shaomei Xu Yanling Fan Bing Lin Continuing Education Service Center Shandong Institute Of Commerce and Technology Jinan Shandong China

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

In intelligent systems, as the amount of data increases, how to analyse data and extract abnormal information is an important task. Based on this, in order to improve the detection efficiency and accuracy of abnormal data in intelligent systems, this study proposes a new and efficient abnormal data mining and detection algorithm. In the initial modelling process, this study introduced the minimum modification cost principle based on cross entropy for modelling. This algorithm optimizes the time series data cleaning process, thereby removing noise data while maintaining the integrity of the data sequence. Then, the extreme gradient boosting algorithm is combined with the Gaussian kernel function to achieve data dimension reduction. This operation can reduce data dimensions and feature redundancy. Finally, the overall anomaly detection operation by integrating the improved Ada boost algorithm with the sampling balance technology can improve the accuracy of the classifier while solving the problem of unbalanced data. The experimental results show that the proposed method is superior to the traditional algorithms in comprehensive indicators.

关键词： Sentiment analysis Accuracy Time series analysis Redundancy Noise Classification algorithms Intelligent systems detection algorithms Kernel Optimization

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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...

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

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

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

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

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

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

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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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