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检索条件"主题词=Detection Algorithms"

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Lightweight detection method of distracted driving based on YOLO-SSC

Lightweight detection method of distracted driving based on ...

引用

Robotics, Intelligent Control and Artificial Intelligence (RICAI), International Conference on

作者： Wuxier Balatijiang Xia Wei College of Electrical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798331541699

ISBN: (纸本)9798331541705

To address the problems of high device requirements, low detection accuracy, and high miss detection rate of overlapping targets in traditional distracted driving target detection models, a vehicle target detection algorithm called YOLO-SSC, based on the improved YOLOv8, was proposed. First, the main body network was reconstructed by using multi-scale fusion. Then, the StarBlock module was used to improve the C2f module in the neck, achieving multi-scale feature fusion to improve the model's detection accuracy for different size and distance targets. Finally, a lightweight model with shared convolution was used to further reduce the model's parameter number and computational cost. The experimental results show that the mAP50 of the improved model is 96.3%, the number of model parameters and the amount of model calculation are 1.4M and 4.5G, respectively, which are reduced by 53.3%and 44.4%compared with YOLOv8n.

关键词： Accuracy Convolution Computational modeling Object detection Feature extraction Real-time systems Neck detection algorithms Robots Intelligent control

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Multi-Channel Attention Mechanism Hot Spot detection Algorithm Based on YOLOv5

Multi-Channel Attention Mechanism Hot Spot Detection Algorit...

引用

International Conference on Electronic Engineering and Informatics (EEI)

作者： XiangZhe Xin Ming Li Qingkai Lang Qiong Wu BEIJING GUOWANG FUDA SCIENCE AND TECHNOLOGY DEVELOPMENT CO. LTD. Beijing China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

To solve the problems of scarcity of hot spot image data and insufficient feature extraction of photovoltaic modules, a multi-channel attention mechanism hot spot detection algorithm based on YOLOv5 is proposed. The hybrid data augmentation method of grid masking and image elastic deformation is used to effectively expand the data of a small number of existing hot spot datasets, while the multi-channel attention mechanism is used to interact with cross-latitude information to obtain the features of hot spot images. The average precision 50 (AP50) of the improved algorithm is improved by 1.17% compared to the baseline model YOLOv5, and its inference speed reaches 100.12 frames/s. It is demonstrated through attentional ablation experiments that capturing the interaction between channel attentions is important for improving the accuracy of the model.

关键词： YOLO Attention mechanisms Accuracy Deformation Feature extraction Data augmentation Inference algorithms Solar panels detection algorithms Informatics

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Seizure detection Based on Vagus Nerve Activity: Translation to an Implantable Setting

Seizure Detection Based on Vagus Nerve Activity: Translation...

引用

IEEE International Workshop on Medical Measurement and Applications (MEMEA)

作者： Michel-Antony Ngan Yamb Javier Chávez Cerda Louis Vande Perre Riëm El Tahry Antoine Nonclercq BEAMS department Université Libre de Bruxelles Brussels Belgium Institute of Neuroscience Université Catholique de Louvain Brussels Belgium

ISBN: (数字)9798350307993

ISBN: (纸本)9798350308006

Epilepsy is a chronic disease that affects fifty million people worldwide. One-third of patients are not responding to antiepileptic drugs. For those patients, if surgery is impossible, vagus nerve stimulation (VNS) can be offered as an adjunctive treatment. As on-demand stimulation improves VNS efficacy, closed-loop VNS – i.e., stimulating at the occurrence of a seizure – emerges as a promising solution for those patients. However, a suitable biomarker for seizure detection is required. Besides, to be clinically valuable, computations related to such a biomarker should be low enough to be embedded in an implant. This work explores the use of vagus nerve electroneurogram (VENG) as a biomarker and compares the performances of various seizure detection algorithms. We analyzed data from six rats that exhibited acute seizures induced by pentylenetetrazol (PTZ), along with three control rats infused with a saline solution. All seizures could be detected with no false positives, even without requiring large computations. This work opens ways to reduce the computation power necessary for VENG-based seizure detection, aiming at embedding a seizure detection algorithm in an implant. Further work should address whether the performance of low computation power algorithms is maintained in a chronic setting, i.e., in the presence of movement artifacts.

关键词： Drugs Surgery Epilepsy Implants Rats detection algorithms

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detection Methodology for Multistage Voltage Sag Based on Residual Analysis

Detection Methodology for Multistage Voltage Sag Based on Re...

引用

International Conference on Harmonics and Quality of Power

作者： Shunyi Li Ying Wang Huaxi Yu Xianyong Xiao Haishan He College of Elec. Engr. Sichuan University Chengdu China

ISBN: (数字)9798350382563

ISBN: (纸本)9798350382570

The monitoring of voltage sags is a crucial prerequisite for mitigating their harmful effects. With the energy transition in power systems and the integration of a large number of distributed generations, multi-stage voltage sags (MSVS) are becoming increasingly common, causing more severe and profound impacts on the grid and users. Currently, there is a lack of effective description and detection methods for MSVS, posing challenges to their statistics, management, and prevention. Due to the varying residual voltages and multiple transition moments of MSVS, traditional methods struggle to effectively detect them. To address these difficulties, this study proposed a novel detection method based on residual analysis, tailored for the special characteristics of MSVS. First, relevant terms for MSVS were introduced, providing a foundation for the monitoring and management of MSVS. Secondly, a detection algorithm based on residual analysis is proposed, achieving accurate identification of MSVS and precise detection of their mutation moments. The effectiveness and accuracy were validated with data from a 10kV distribution network. The proposed method lays the foundation for improving the power quality of the existing energy transition system.

关键词： Accuracy Voltage fluctuations Prevention and mitigation Power quality Distribution networks Power system harmonics Harmonic analysis Power grids detection algorithms Monitoring

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Ultrasound Image Guided Robotic Puncture System With Needle Tip detection and Deviation Correction Features

Ultrasound Image Guided Robotic Puncture System With Needle ...

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IEEE International Conference on Robotics and Biomimetics

作者： Jinhang Li Zhengnan Jiang Chenyang Li Baoliang Zhac Peng Zhang Pak Kin Wong Ying Hu Department of Electromechanical Engineering University of Macau Macau China Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Robotic needle insertion with ultrasound image guidance can help improve the accuracy of liver cancer biopsy. Most robotic puncture systems with ultrasound guidance utilize dual-arm configurations, which are less efficient in terms of space utilization. To address this, we have developed a compact ultrasound image guided needle puncture mechanism. It consists of an ultrasound probe to provide image feedback and a serial-parallel mechanism together with a range extender to drive the puncture needle. To achieve accurate puncture in the dynamic environment of tissue deformation and needle bending, a needle tip detection algorithm in ultrasound images with YOLO V8 network is proposed, together with needle axis fitting and Kalman Filter to furtherly improve the accuracy. With image feedback, a closed-loop deviation correction controller with fuzzy control algorithm is developed and tested on the robotic puncture system, achieving 0.83mm accuracy, which can meet the clinical requirement.

关键词： YOLO Ultrasonic imaging Accuracy Heuristic algorithms Fitting Needles Robot sensing systems Kalman filters Probes detection algorithms

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Classroom Automation System Using Human detection Based on YOLO Algorithm and IoT Technology

Classroom Automation System Using Human Detection Based on Y...

引用

Electrical and Information Technology (IEIT), International Conference on

作者： Mazaya Muhammad T. S. El Syaff Vincentius Jason Samhadi Brahmantya Aji Pramudita School of Electrical Engineering Telkom University Bandung Indonesia The University Center of Excellence for Intelligent Sensing-IoT Telkom University Bandung Indonesia

ISBN: (数字)9798331516888

ISBN: (纸本)9798331516895

Classroom is an important facility in the school or university to be utilized as a learning space and lessons happen. Students and teachers meet in the classroom on a daily, with different schedules. Hence, every class needs to be monitored and controlled for finding out the class conditions. However, the staffs have to monitor the classroom by checking the classroom one by one. Thus, it is not efficient and increase the workload if there are many classes to be checked. Therefore, automated technology can be implemented to reduces the workload and increases staff efficiency when controlling and monitoring the classroom, especially in the context of time. In this study, an IoT-based system was proposed as a solution to control the classroom condition. This system used class schedules and human presence as a parameter to control the classroom. A camera utilized to detect the object and performed YOLOv8 algorithm to analyze the condition. in this system, firebase was leveraged to store every data from detecting process. Then, the data was used for controlling the actuators, such as door locking, lamp, and fan, which were controlled by ESP32. The result showed that the proposed system could perform with accuracy of 57%, sensitivity of 67%, and positive predictive value of 79%. The proposed system can achieve a success rate of 90% with less than 20 second of time delay which makes this system time effective. But it is not yet known whether this system will work effectively in low light conditions.

关键词： YOLO Schedules Fans Accuracy Sensitivity Prediction algorithms Data transfer Information technology detection algorithms Monitoring

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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...

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

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

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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...

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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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