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检索条件"主题词=Detection Algorithms"

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An Improved YOLOv5 Based Smoke detection Algorithm for Power Room

An Improved YOLOv5 Based Smoke Detection Algorithm for Power...

引用

Energy Engineering and Power Systems (EEPS), International Conference on

作者： Jinglong Zhao Jizhao Lu Yuehao Zhou Yanan Liu Lijuan Xu Information and Communication Branch of State Grid Henan Electric Power Company Zhengzhou China

ISBN: (数字)9798350375138

ISBN: (纸本)9798350375145

To detect smoke in the power room, a small target smoke detection algorithm based on improved YOLOv5 is proposed in this paper. Firstly, the feature fusion attention (FFA) module is introduced into the backbone network to focus the model on extracting smoke feature information from small targets. Secondly, by replacing the convolutional layer module with a multi-scale pyramid decoupled head (MPDH) module, the detection part of the prediction head layer in the YOLOv5 algorithm is improved to enhance the positioning accuracy of small target smoke. Finally, experimental validation is conducted on proprietary datasets. The experimental results show that the algorithm proposed in this paper achieves 85.4 % accuracy in object detection. In terms of accuracy and recall, the proposed algorithm has improved by 3.2% and 6.3% respectively compared to the original algorithm.

关键词： YOLO Accuracy Predictive models Feature extraction Prediction algorithms Power systems Data mining detection algorithms Power generation

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A Visual Localization Method Based on Multi-scale Crater detection and Cluster Matching

A Visual Localization Method Based on Multi-scale Crater Det...

引用

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

作者： Siyuan Li Jianbin Huang Tao Li Shuo Zhang Jiaqiong Ren Jiaxuan Wu Yuntao He School of Electronic Information Engineering Beihang University Beijing China School of Instrumentation and Opto-electronic Engineering Beihang University Beijing China Beijing Institute of Control Engineering Beijing China

ISBN: (数字)9798331518677

ISBN: (纸本)9798331518684

Visual localization is a critical component of lunar exploration, it helps avoid hazardous regions on the Moon's complex surface. This paper proposes a novel method based on multi-scale crater detection and cluster matching to address the limitations of existing visual localization methods in terms of real-time performance and accuracy. The method uses the Hough transform to detect large-scale craters in simulated images, calculates the center offsets between the detected large-scale craters and those in the candidate crater list, and applies K-means clustering to group the center offsets, forming large-scale matched pairs. These large-scale matched pairs are then used for pose estimation, achieving rapid coarse localization and obtain the coarse localization error. Subsequently, principal component analysis (PCA) is employed to identify small-scale craters, similarly clustered and matched using K-means. Finally, estimate the pose again and compute the reprojection error for precise localization. Simulation experiments demonstrate that the proposed method achieves a reprojection error of 2.35 pixels (px) and a relative error of only 0.11%, validating the method's effectiveness.

关键词： Location awareness Visualization Accuracy Moon Pose estimation Buildings Transforms Real-time systems detection algorithms Principal component analysis

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Research on Wheat Ear detection Algorithm Based on Improved YOLOv8

Research on Wheat Ear Detection Algorithm Based on Improved ...

引用

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

作者： Chunyu Zhang Sitong Liu Zeran Hao Datian Niu School of Science Dalian Minzu University Liaoning China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Deep learning techniques are of significant importance in the detection of wheat ears in the field of smart agriculture, bringing innovation and improvement to the agricultural sector. How to accurately detect wheat ears has become a challenging task due to the high density, diversity, and complex growth cycle of wheat ears, as well as the complex background of the detection process, which may result in false positives, false negatives, and low detection rates. In this paper, a novel wheat spike detection technique is proposed, which is optimized on the basis of YOLOv8. First, RepNCSPELAN4 is introduced into the backbone network to replace the original C2F convolution, which enhances the feature extraction ability of the model. Second, a new MSAM module is introduced into the Bottleneck of the neck C2f module to construct a new C2fMSAM module; finally, the original loss function is replaced by the normalized Wasserstein distance loss. The experimental results show that the accuracy, recall and average accuracy of the improved YOLOv8 model are improved by 1.1%, 3.5% and 2%, respectively, compared with the baseline model.

关键词： Smart agriculture Deep learning Technological innovation Accuracy Biological system modeling Computational modeling Ear Object detection Magnetic heads detection algorithms

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A Review of Lane Line detection Research

A Review of Lane Line Detection Research

引用

International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Hao Wang Yuyang Gu Shanghai Institute of Technology SIT Shanghai China

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Lane line detection is an important technology in the field of intelligent driving system, which has a significant impact on the development of intelligent driving system. This paper reviews the research progress of lane line detection algorithms, describes the process of traditional lane line detection algorithms and deep learning based lane line detection algorithms respectively, and elaborates the representative implementation methods. At the same time, this paper also describes in detail some public datasets commonly used for lane line detection tasks. Finally, some existing problems of lane line detection are summarized, and the development direction of lane line detection research is prospected.

关键词： Deep learning Reviews Training data Focusing detection algorithms Informatics Meteorology

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Fire detection algorithm with multi-scale feature focus and diffusion

Fire detection algorithm with multi-scale feature focus and ...

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Electronics and Devices, Computational Science (ICEDCS), International Conference on

作者： Qi Xiangming Zhang Yiran School of Software Engineering Liaoning Technical University Huludao China

ISBN: (数字)9798331527624

ISBN: (纸本)9798331527631

Flame and smoke detection in fire detection systems can suffer from low accuracy and high false detection rates due to background and environmental factors. To address this, a multi-scale feature focusing and diffusion multilayer fusion fire detection algorithm is proposed, using YOLOv8s as the baseline model. This approach introduces large separable convolutional kernel attention at the end of the backbone network to expand the receptive field, enhance the understanding of spatial feature relationships, and improve feature extraction capabilities. The neck network is enhanced by refining the PANet structure, adding a feature focusing module for multi-scale feature fusion, and incorporating a feature diffusion mechanism to boost the model's utilization of global context information and multi-layer feature integration. The detection head is upgraded with a dynamic detection head featuring unified attention, which consolidates three types of attention to improve detection accuracy. After algorithm optimization and testing, the improved model shows a 1.4 percentage point increase in mAP@0.5, a 2.6 percentage point increase in mAP@0.5:0.95, and a detection speed of 84 fps, outperforming other mainstream object detection algorithms.

关键词： Accuracy Scientific computing Heuristic algorithms Refining Fires Focusing Feature extraction detection algorithms Optimization Testing

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Automatic Modulation detection in Non-orthogonal Multiple Access Systems

Automatic Modulation Detection in Non-orthogonal Multiple Ac...

引用

International Symposium on Telecommunications, IST

作者： Fatemeh Shabanali Mehrdad Ardebilipour Department of Electrical and Computer Engineering K.N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798350356250

ISBN: (纸本)9798350356267

Increasing demand and spectrum limitations pose ongoing challenges in telecommunications that constantly engage researchers in this field. Recently, various wireless systems have been developed, including Non-Orthogonal Multiple Access (NOMA), which is proposed for 5G and 6G networks. NOMA improves capacity while maintaining spectral resources by allowing the transmitter to send predefined user symbols with user-specific power over the same resources. The conventional NOMA receiver, Serial Interference Cancellation (SIC), requires knowledge of the modulation types to separate user signals. To improve spectral efficiency and reduce signaling overhead issues, an Automatic Modulation detection algorithm has been proposed. The algorithm enables the receiver to automatically identify the modulation type, eliminating the need for the transmitter to provide this information. This paper presents a novel automatic modulation detection algorithm for a two-user NOMA system that uses feature extraction and deep learning to achieve high detection rates while maintaining manageable computational complexity. The proposed algorithm outperforms previous methods, achieving 93% detection rate at 5 dB signal-to-noise ratio, compared to 78.36% reported in previous studies.

关键词： Deep learning Wireless communication NOMA Interference cancellation Transmitters Modulation Receivers Feature extraction Telecommunications detection algorithms

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Bed-Exiting Behavior detection in Sheeting IoT System with Printed Electrode

Bed-Exiting Behavior Detection in Sheeting IoT System with P...

引用

International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Yuki Sato Akinori Ueno Department of Electric and Electronic Engineerig Tokyo Denki university Tokyo Japan

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

In this study, we propose a novel Internet of Things (IoT) system with a printed sheet electrode and evaluate its detection ability for bed-exiting behavior. The sheet electrode placed under the bed sheet detects the body proximity of the shoulder and lower back independently through the worn clothes and bed sheet based on capacitive coupling. A detection algorithm implemented in a single-board computer was tested in a bed-exiting experiment with five males wearing restraints to simulate the motions of the elderly. Compared with a commercially available mat sensor, the mean detection time of the proposed system was significantly shorter $(\mathrm{p} < 0.05)$ for each subj ect and 3.5 s earlier on average. In addition, there were no false detections among 95 postural changes and 50 bed-exiting behaviors in the experiment.

关键词： Electrodes Couplings Body sensor networks Internet of Things Older adults detection algorithms

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Electric Energy Meter Reading detection Algorithm Based on Improved YOLOv8n Model

Electric Energy Meter Reading Detection Algorithm Based on I...

引用

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

作者： Youhu Wang Zijing Wang Jianhua Shi Peiyong Yuan Bobo Xue Shunde Liu Kang Ma Xining Jiuzheng Electronic Technology Co. Ltd Xining Qinghai China Computer Department of North China Electric Power University Baoding Hebei China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

In smart grid systems, automated electric meter reading technology is crucial for optimizing the management and distribution of electricity resources. In this study, an optimized algorithm based on the YOLOv8n model is introduced for electric meter reading recognition, with the goal of improving both the precision and performance of automated meter reading systems. This algorithm incorporates the SPDConv as a substitute for the conventional convolution layers in the original model, which significantly minimizes information loss while boosting the model's feature extraction efficiency. Moreover, the backbone network integrates a lightweight channel attention mechanism known as ECA, which enhances attention to critical local features and improves the detection performance for small objects. The experimental evaluation on a tailored electric meter dataset reveals that the enhanced algorithm surpasses the YOLOv8n model, achieving a 4.6% improvement in mAP, along with reductions of 10.6% in parameter count, 10% in model size, and 7.4% in computational cost.

关键词： Attention mechanisms Accuracy Convolution Computational modeling Interference Feature extraction Boosting Meter reading Smart grids detection algorithms

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Design of a Precision Resolver Signal Measurement Algorithm based on Phase Sensitive detection

Design of a Precision Resolver Signal Measurement Algorithm ...

引用

URSI Atlantic Radio Science Conference (URSI AT-RASC)

作者： Li You Haibin Jin Yalu Li Min Liu Zhenyu Guan Beijing Orient Institute of Measurement and Test Beijing China Beihang University Beijing China

ISBN: (数字)9789463968102

ISBN: (纸本)9798350360257

The article introduce a precision resolver signal measurement algorithm based on phase sensitive detection, which can be applied to the development of resolver calibration systems for angle position indicators or resolver simulators. Based on universal digitizers we realized resolver signal measurement with 0.0001° resolution and $0.001^{\circ} (k=2)$ uncertainty.

关键词： Phase measurement Accuracy Uncertainty Measurement uncertainty Position measurement Calibration detection algorithms

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Terminal Abnormal State Recognition Method Based on Change Point detection of Sequence

Terminal Abnormal State Recognition Method Based on Change P...

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Energy Engineering and Power Systems (EEPS), International Conference on

作者： Lingji Kong Xiaoguang Yi Huadong Zhang Wenxiu Shi Xu Meng Heze Power Supply Company State Grid Shandong Electric Power Company Heze China

ISBN: (数字)9798350366914

ISBN: (纸本)9798350366921

The detection of terminal abnormal state is very important to improve the safety and efficiency of power system. A method was proposed in this article based on sequence change point detection to detect and recognize the abnormal state. Aiming at the characteristics that the level of electricity consumption may increase or decrease, the bilateral CUSUM algorithm based on sliding window was applied in this research. Experiments showed that this method could identify the abnormal state accurately and had high detection accuracy, which provided strong support for the safe operation of power detection terminal.

关键词： Accuracy Electricity Data acquisition Power systems Safety detection algorithms

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