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检索条件"主题词=detection algorithms"

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Lightweight Model Construction for Cotton Terminal Bud detection Based on YOLOv8

Lightweight Model Construction for Cotton Terminal Bud Detec...

引用

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

作者： Jie Zhang Musha Yasenjiang Jipeng Yao School of Mechanical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

In order to reduce the potential hazards of terminal bud inhibitors on cotton plant growth as well as ecological environment and personal health during cotton topping operation, this study constructed a lightweight model based on improved YOLOv8n cotton terminal bud detection by combining the end- side neural network architecture MobileNetV3 network, which further reduces the model computational amount and reasoning speed under the premise of smaller accuracy loss. In addition, up- sampling content-aware features are introduced to reorganize the network structure to improve the accuracy of the model without increasing the number of parameters. The experimental results show that for the cotton terminal bud dataset collected in the cotton field environment, the number of parameters and the model size of the improved model are reduced by 22.22 and 21.95 percentage points compared to the original YOLOv8n model with only 0.3 percentage points reduction in the precision, which is superior to the other comparative target detection algorithms, and it can satisfy the multi-target detection of cotton terminal buds' real-time demand, providing reference for the deployment and application of mobile detection equipment.

关键词： Solid modeling Accuracy Inhibitors Computational modeling Biological system modeling Neural networks Object detection Real-time systems Cotton detection algorithms

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Simulation Analysis of CS-ACELP Speech Compression Algorithm Based on Mute detection Technology

Simulation Analysis of CS-ACELP Speech Compression Algorithm...

引用

Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Bo Zhou Zhaoyang Li Zongju Xiong Wanbin Zhang Communication Systems Department Xichang Satellite Launch Center of China Xichang Sichuan China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Based on the study of CS-ACELP speech compression algorithm and mute detection technology, we propose a new mute detection scheme. Based on short-time average amplitude and short-term average over zero rate, it distinguishes noise, clearing and turbidity through the two-level judgment method. Through simulation analysis, the algorithm is based on CS-ACELP algorithm proposed G.729B, it reduces the complexity and achieves the effect of reducing the code rate.

关键词： Resistance Computer science Codes Turbidity Real-time systems Encoding Complexity theory detection algorithms Compression algorithms Signal to noise ratio

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Lightweight Traffic Accident detection Algorithm Based on Attention Mechanism

Lightweight Traffic Accident Detection Algorithm Based on At...

引用

Intelligent Computing and Control (IC&C), International Conference on

作者： XiaoYan Hou ShiHuan Wen ShengFu Chen JianHe Li Kai Xu ZhenYu Wang WenHan Wu South China University of Technology GuangZhou China PSBC Consumer Finance Company Limited GuangZhou China Tianjin University of Science & Technology TianJin China

ISBN: (数字)9798350351873

ISBN: (纸本)9798350351880

Traffic accident detection is a challenging task in intelligent transportation systems. Rapid and accurate detection of traffic accidents can ensure the safety of people's lives and property to the greatest extent. However, the existing accident detection models have some limitations such as slow speed or low precision, which limit the practical application. Aiming at the current low accuracy of rapid traffic accident detection, this paper proposes a traffic accident detection algorithm based on attentional feature fusion, which combines the feature extraction and fusion process with the attention mechanism. Specifically, the MHSA module is integrated into the Backbone stage of the YOLOv5 network, and the CBAM module is integrated into the Neck stage to refine and capture image features and enhance network feature extraction and fusion capabilities. At the same time, in view of the lack of traffic accident image data, this paper establishes a traffic accident image data set. Experiments were carried out on the traffic accident image data set and the results show that, under the condition that the detection speed of each image is guaranteed to be 9.7ms, the accuracy of the proposed algorithm to identify traffic accidents is 4% higher than that of YOLOv5s. It achieves a balance between speed and accuracy of traffic accident detection under limited computational cost.

关键词： YOLO Accuracy Attention mechanisms Computational modeling Feature extraction Neck Safety detection algorithms Intelligent transportation systems Accidents

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Multi-level Fast Screening Loop Closure detection Algorithm that Integrates the Intensity and Geometry Information of Point Clouds

Multi-level Fast Screening Loop Closure Detection Algorithm ...

引用

China Instrument and Control Society (ACCIS), Academic Conference of

作者： Chuan Zhang Xuefen Zhu Zhengpeng Lu Gangyi Tu School of Instrument Science and Engineering Southeast University Nanjing China School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing China

ISBN: (数字)9798350350326

ISBN: (纸本)9798350350333

In Simultaneous Localization and Mapping (SLAM) systems, LiDAR odometry inevitably has errors when matching inter-frame point clouds, forming a cumulative error over extended periods of operation to lead to the degradation of localization building accuracy. The closed-loop optimization strategy triggered by the loop closure detection algorithm is a key method to reduce the cumulative error. Therefore, in this paper, a multilevel fast sieve loop closure detection algorithm that synergizes point cloud intensity and geometric information is designed. The global identification of loop closure frames is accelerated by employing dimensionality reduction on the intensity feature of point clouds after filtering out the ground point clouds. Then the local plane key points are extracted from the candidate frames for secondary verification to obtain the best loop closure frame. The experiments on the online unstructured dataset show that the precision-recall (PR) curve of this paper's algorithm covers the widest area, with a maximum F1 score of 0.93, showing a better performance compared with the existing loop closure detection algorithms. The root mean square error (RMSE) of the absolute trajectory error was reduced to 0.68 meters after optimization, representing a performance improvement of approximately 48%, and the results of the point cloud construction are more accurate and precise.

关键词： Point cloud compression Location awareness Simultaneous localization and mapping Accuracy Feature extraction Trajectory Odometry detection algorithms Root mean square Optimization

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Spoofing detection Algorithm for Tightly Coupled INS/GNSS Integration Model with robust Averaging Measurement

Spoofing Detection Algorithm for Tightly Coupled INS/GNSS In...

引用

Signal, Information and Data Processing (ICSIDP), IEEE International Conference on

作者： He Chengyan Wu Luoyu Zhaolin Zhang Wang Ling Tao Mingliang School of Electronics and Information Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Global Navigation Satellite Systems (GNSS) are susceptible to external signal *** GNSS-INS tightly coupled model exhibits high robustness and can avoid some interference; however, it is ineffective against spoofing interference, especially slow-changing spoofing interference. This paper proposes a robust detection algorithm based on measurement averaging and the IGG-III weighting function to effectively identify and mitigate such interference. By constructing a detection statistic from the measurement-averaged innovation, we theoretically demonstrate its nature as a zero-mean white noise process and design a corresponding adaptive detection strategy. Compared with traditional spoofing interference detection methods, simulation experiments using an open-source dataset show that the proposed algorithm significantly improves the detection rate of slowly varying spoofing signals while maintaining a low false alarm rate, validating its effectiveness and robustness in tightly coupled INS/GNSS systems.

关键词： Weight measurement Technological innovation Global navigation satellite system Simulation Interference White noise Particle measurements Robustness Noise measurement detection algorithms

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Abnormal Network Traffic detection Algorithm Based on Improved Convolutional Neural Network

Abnormal Network Traffic Detection Algorithm Based on Improv...

引用

Interactive Intelligent Systems and Techniques (IIST), International Conference on

作者： Lu Chen Tao Zhang Yuanyuan Ma Mu Chen Protection and Monitoring Technology State Grid Smart Grid Research Institute Co. Ltd Nanjing China Protection and Monitoring Technology State Grid Laboratory of Power Cyber-Security Nanjing China

ISBN: (数字)9798350374421

ISBN: (纸本)9798350374438

With the increasing complexity, automation, and intelligence of network attacks, new types of attacks are constantly emerging in the network, posing great challenges to network attack detection and timely response based on prior rules. In order to more effectively and accurately identify abnormal traffic, a network abnormal traffic detection algorithm based on improved convolutional neural networks is proposed. The algorithm first inputs the key features of traffic anomaly monitoring, converts them into color images through visualization technology, extracts higher dimensional features, and then uses neural structure search technology to find a lightweight convolutional network structure with high accuracy and less time consumption. The optimal model is used to output the type of traffic anomaly. The results indicate that the algorithm has a higher accuracy and the shortest classification time for a single image.

关键词： Visualization Accuracy Convolution Telecommunication traffic Reinforcement learning Predictive models Feature extraction Convolutional neural networks detection algorithms Monitoring

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Temporal Convolutional Network for Gait Event detection

Temporal Convolutional Network for Gait Event Detection

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Hassan Ashraf Cédric Schwartz Asim Waris Olivier Brüls Mohamed Boutaayamou Laboratory of Movement Analysis (LAM-Motion Lab) University of Liège Liège Belgium Department of Biomedical Engineering and Sciences School of Mechanical and Manufacturing Engineering (SMME) National University of Sciences and Technology (NUST) Islamabad Pakistan

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

In this study, we propose a novel deep learning-based framework for automatic gait event detection (GED) in diverse and complex walking scenarios, aiming to address the challenge of accurately identifying biomechanical markers linked to movement disorders. Current research methods in GED frequently encounter difficulties in maintaining high accuracy levels, especially when confronted with the intricate challenges of daily-life activities. Our proposed framework employs a Temporal Convolution Network (TCN) to address GED across diverse walking conditions and environments. As a post-processing step, we introduce a peak detection algorithm on the outputs of the TCN to accurately identify the gait events. The effectiveness of this framework is evaluated using a publicly available gait dataset, reporting event detection rates and time agreement through F1 score and Mean Absolute Error (MAE) for Heel Strike (HS) and Toe-Off (TO) events. The proposed methodology achieves a mean F1-score of 0.96 ± 0.07 and 0.92 ± 0.11 for HS and TO, respectively, indicating consistent performance. Similarly, the time agreement results in a mean MAE of 6.25 ms ± 3.67 ms and 16.87 ms ± 11.56 ms for HS and TO across all indoor and outdoor activities. These findings highlight the robustness of the proposed framework in GED under varying conditions, suggesting its potential for various pathological scenarios in daily-life activities.

关键词： Legged locomotion Pathology Accuracy Event detection Convolution Robustness Gyroscopes detection algorithms Standards Foot

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Human Activity detection Algorithm Based on TSACT Model

Human Activity Detection Algorithm Based on TSACT Model

引用

Chinese Automation Congress (CAC)

作者： Zhiheng Xia Yongli Yang School of artificial Intelligence Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Human activity recognition technology based on WiFi has attracted considerable attention in health monitoring and medical treatment due to its characteristics, including the absence of additional equipment, strong privacy protection, and comprehensive coverage area. However, due to the high complexity involved in WiFi signal detection and human activity, the recognition rate of human activity based on WiFi signal detection is not ideal. To effectively explore the mapping relationship between WiFi signal variations and human activity, this paper proposes a TSACT model(Combined Time and Space Domain Information for Human Activity detection). The TSACT model recognizes that CSI signals encompass both temporal and spatial feature information, and relying on information from only one dimension may result in the omission of critical data, thereby affecting the final activity recognition rate. Therefore, TSACT integrates temporal and spatial characteristics to identify human activities. In the conclusion of the paper, the recognition rates of human activities using temporal features, spatial features, and the TSACT model are compared. The proposed scheme achieves a higher recognition rate.

关键词： Privacy Medical treatment Feature extraction Data models Human activity recognition Protection detection algorithms Wireless fidelity Signal detection Monitoring

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Improved YOLOv8n Helmet detection Algorithm

Improved YOLOv8n Helmet Detection Algorithm

引用

Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Changcong Zhang Yan Xu School of Information Science and Engineering Linyi University Linyi Shandong China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Currently in complex scenarios, most existing helmet detection algorithms face challenges such as small target leakage, false detections, and low accuracy. In response, a novel helmet detection model named YOLOv8n-PSB is proposed, which improves upon the YOLOv8n model. To address the shortcomings of existing algorithms, several enhancements have been introduced in the YOLOv8n-PSB model. Firstly, a small target detection layer P2 has been incorporated into the backbone network to improve the learning of small target features by enhancing the fusion of shallow and deep semantic information. Additionally, the SPD-Conv module has been integrated to sample the feature map effectively without losing valuable information, thereby improving the detection of low resolution and small targets. Furthermore, the BiFPN bi-directional feature pyramid structure has been utilized to enhance multi-scale feature fusion, reduce background interference, and improve detection accuracy. Experimental results demonstrate that the YOLOv8n-PSB model achieves an average precision of 94.5%, with improvements in average recall, mAP0.5, and mAP0.5:0.95 by approximately 2% compared to the original YOLOv8n model. Overall, the enhanced YOLOv8n-PSB algorithm effectively enhances helmet detection in complex scenarios by reducing missed detections and false alarms for small targets, surpassing the performance of other common helmet detection algorithms.

关键词： Head Accuracy Computational modeling Semantics Object detection Interference Feature extraction Production facilities Safety detection algorithms

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Early Results on Water detection in SWOT HR Images

Early Results on Water Detection in SWOT HR Images

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Nicolas Gasnier Roger Fjørtoft Brent Williams Damien Desroches Lucie Labat-Allée Jérôme Maxant Centre National d’Etudes Spatiales Toulouse France Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA CS-Group Toulouse France ICUBE-SERTIT Université de Strasbourg France

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Water detection is a key step in the operational processing of KaRIn HR images from the SWOT mission, which provides water surface extent and elevation for continental water surfaces globally and repeatedly. A new water detection method has been developed for this sensor due to its specific characteristics. In this article we present examples of detected water masks and preliminary assessments of their accuracy. We also provide perspectives for further improvements in the water detection algorithm.

关键词： Water Accuracy Geoscience and remote sensing detection algorithms Surface treatment

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