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检索条件"主题词=Detection Algorithms"

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DGL-YOLOv8 a Lightweight Yarn detection Algorithm

DGL-YOLOv8 a Lightweight Yarn Detection Algorithm

引用

Chinese Automation Congress (CAC)

作者： Shangjie Li Ren Jia Jiachao Dai School of Information Science & Engineering Zhejiang Sci-Tech University Hangzhou China Zhejiang Sci-Tech University Hangzhou China Changshan Research Institute Co. Ltd. Quzhou China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Yarn, being a fundamental raw material in fabric manufacturing, directly influences the quality of the final product. To address potential challenges that may occur at various stages of yarn production, in this paper, we propose a lightweight and efficient recognition algorithm based on YOLOv8n, named DGL-YOLOv8. First, we enhanced the lightweight convolution DWConv by incorporating the MLCA attention mechanism to strengthen the connection between channel and spatial information. Then, we introduced the GhostModule lightweight module to refine the C2f structure, replacing the standard convolution layers with dynamic convolution layers. This allows the model to better capture features of objects at different scales. Finally, in the detection head, we adopted the concept of shared parameters, replacing the bulky three Detect modules of the original model with a single shared convolution, further reducing the model's parameters and computational cost. According to the experimental data, DGL-YOLOv8 achieved an mAP0.5 value of 90.1%, an improvement of 0.5% over YOLOv8n. The parameter count is 0.95M, and the computational load is 3.3 GFLOPs, representing reductions of 68% and 59%, respectively, compared to the base model. The results demonstrate that the improved model greatly increases the effectiveness of real-time object detection in complex environments, providing an effective solution for yarn production monitoring.

关键词： Adaptation models Attention mechanisms Convolution Computational modeling Production Feature extraction Computational efficiency Yarn detection algorithms Load modeling

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Research on Fire Smoke detection Algorithm Based on YOLOv8

Research on Fire Smoke Detection Algorithm Based on YOLOv8

引用

International Conference on Advanced Computer Theory and Engineering, ICACTE

作者： Xianglong Han Qingwei Shen Jin Huang Yuan Zhang school of Electronic and Information Engineering Anhui Jianzhu University Hefei China

ISBN: (数字)9798331539887

ISBN: (纸本)9798331539894

The SDE-YOLOv8 fire detection model is proposed to solve the problems of low accuracy and slow speed in the detection of fire *** on YOLOv8, SimAM attention mechanism is introduced in this model, and the C2f layer in the network structure is improved to obtain D-C2f module, which enhances the feature expression ability of the model in complex scenes and inhibits the interference of irrelevant ***, in order to improve the speed of model detection, a lightweight backbone network EfficientViT was used for feature extraction, which reduced the computing overhead during detection. The experimental results show that compared with the original YOLOv8 on DFS data set, SDE-YOLOv8's average accuracy mAP50 is increased by 2.2%, and the detection speed is increased by 14.3%, achieving a good improvement in accuracy and speed.

关键词： Accuracy Attention mechanisms Image color analysis Computational modeling Fires Interference Feature extraction Real-time systems detection algorithms

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Lightweight Road Vehicle detection Algorithm Based on Improved YOLOv8

Lightweight Road Vehicle Detection Algorithm Based on Improv...

引用

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

作者： Xin Zhang

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

This paper addresses the problems of large model size, many parameters, high equipment requirements, and low recall rate in traditional vehicle detection models, and proposes a lightweight road vehicle detection algorithm based on the improved YOLOv8 (You Only Look Once). The improved detection algorithm reduces the number of parameters and the size of the model, which can effectively improve the detection speed and shorten the training time. This paper proposes a lightweight Shared Parameter detection head (SPDH), which can greatly reduce the number of model parameters while ensuring the detection accuracy. in the meantime SimAM attention mechanism, a lightweight and parameterless attention mechanism of convolutional neural networks, is added to the network to enhance the detection performance. Finally, the Star Block in StarNet proposed by Microsoft in 2024 is used to replace the Bottleneck in C2f for further lightening and guarantee performance. Experiments on BIT-Vehicle dataset demonstrate that the superior performance of the algorithm compared to the original model. Under the premise that The parameter count has been decreased by approximately 35% and the model’s size has been reduced by around 34%, resulting in a 1.2% increase in precision and a 1.8% increase in recall, and other evaluation indicators are roughly consistent with the benchmark model. The excellence of the improved algorithm is proved.

关键词： Training YOLO Information science Attention mechanisms Vehicle detection Road vehicles Stars Approximation algorithms Convolutional neural networks detection algorithms

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Silent Face Anti-Spoofing detection Algorithm Based on Meta-learning and Attention Mechanism

Silent Face Anti-Spoofing Detection Algorithm Based on Meta-...

引用

Advances in Electrical Engineering and Computer Applications( AEECA), 2020 IEEE International Conference on

作者： Lan Li Hongzhe Dou Zijia Yu Xujuan Zhang Lijuan Sun Jundi Wang School of Computer Science & Artificial Intelligence Lanzhou Institute of Technology Lanzhou China

ISBN: (纸本)9798350355260

In the field of facial identity authentication, silent face anti-spoofing detection, as a key technology, to prevent fake photos and videos, is receiving widespread attention. To address the issue of low detection accuracy in existing algorithms that similar tasks have not appear in training, silent face anti-spoofing detection algorithm based on meta-learning and attention mechanism is proposed. First, by leveraging meta-learning to quickly adapt to shot samples, the proposed algorithm uses MAML as the main network to capture the commonalities between training tasks, allowing it to quickly adjust parameters in new scenarios or new attack types to achieve efficient detection. Then, in order to focus the model on extracting and analyzing the key features of facial regions, an attention mechanism is introduced in MAML to reduce the interference from the facial background region. Finally, a Fourier spectrum aided supervision branch is designed to improve the accuracy of classification by using the difference of the Fourier spectrum of real/fake face in the frequency domain. Experimental results show that the proposed algorithm achieves good accuracy on public NUAA dataset, with AUC and F1 scores of 0.99 and 0.90, respectively.

关键词： Metalearning Training Analytical models Adaptation models Accuracy Attention mechanisms Interference Feature extraction detection algorithms Faces

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Belt Abnormal High-Temperature detection Algorithm Based on Improved YOLOv7-Tiny

Belt Abnormal High-Temperature Detection Algorithm Based on ...

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Lu Xiao Guolei Chen Yingjie Man Wendong Xiao Liang Yuan Qing Tao China University of Mining and Technology Xuzhou China Xinjiang Zhundong TBEA Energy Co. Ltd. Changji China Xinjiang Key Laboratory of Intelligent Production and Control of Open Pit Mines Changji China School of Mechanical Engineering Xinjiang University Urumqi China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

This paper endeavors to enhance the current coal conveyor belt anomalous high-temperature detection algorithms by addressing their limitations in terms of precision and efficiency. We introduce a novel algorithm grounded in the optimized YOLOv7-tiny model, which leverages the Normalized Wasserstein Distance (NWD) loss function to enhance the representation of small targets. Furthermore, we incorporate the DyHead detection head to enrich feature representation and elevate detection capabilities. Our experimental findings validate that our enhanced algorithm surpasses the original YOLOv7-tiny algorithm, exhibiting notable gains in both detection accuracy and speed, especially when detecting small and visually similar targets.

关键词： Accuracy Automation Fuses Coal Belts Feature extraction detection algorithms Information technology

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An Efficient Cluster-Based Asynchronous Activity detection Algorithm for Cell-Free Massive MIMO Systems

An Efficient Cluster-Based Asynchronous Activity Detection A...

引用

International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Chenhao You Ziheng Li Tong Wang Lin Gao Yufei Jiang School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology School of Electronics and Information Engineering Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

In this paper, we propose an efficient cluster-based activity detection algorithm for asynchronous grant-free random access (GFRA) cell-free massive multiple-input multiple-output (CF-mMIMO) systems. To address the uncertain delay challenge in the asynchronous transmission scenario, we aim to enhance the accuracy of active user devices detection and meanwhile reducing the computational complexity of the detection process by utilizing an efficient cluster-based block coordinate descent (BCD) activity detection algorithm. This challenge is heightened in the context of ultra-reliable low-latency communications (URLLC), where precise and timely detection of active users is crucial. To enhance the efficiency of the detection process, we investigate an active set strategy to reduce the number of potential active user devices requiring identification. The accuracy of active user device detection for asynchronous GFRA in CF-mMIMO systems is significantly enhanced by the implementation of the proposed cluster-based BCD algorithm with active set. Simulation results demonstrate that the proposed algorithm achieves superior detection performance with lower computational complexity compared to the traditional algorithms.

关键词： Wireless communication Performance evaluation Accuracy Simulation Signal processing algorithms Clustering algorithms Ultra reliable low latency communication Signal processing detection algorithms Computational complexity

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UAV-based infrard thermography for the fine highvoltage insulators detection

UAV-based infrard thermography for the fine highvoltage insu...

引用

Big Data & Artificial Intelligence & Software Engineering (ICBASE), International Conference on

作者： Lihong Xu Xinyu Liu State Grid Fujian Electric Power Company Putian China School of Electrical Engineering and Automation Fuzhou University Fuzhou China

ISBN: (数字)9798331506612

ISBN: (纸本)9798331506629

Toward the fine detection of high-voltage insulators, this paper proposes a framework for fine detection of high-voltage insulators based on infrared thermal imaging data. The framework realizes high-precision detection of insulators and fine detection of the number of insulator tiles through the improved YOLOv5s detection algorithm combined with the designed density clustering peak detector. The improved YOLOv5s detection algorithm embedded with the designed CDA attention mechanism enhances the global sensing capability. Meanwhile, the density clustering peak detector designed based on density clustering combined with moving smooth filtering realizes the fine counting of insulator tiles in infrared thermal imaging. In order to verify the effectiveness of the proposed framework, an infrared thermal imaging insulator detection dataset from the UAV perspective is built and compared with typical detection models and benchmark models. The experimental results show that the proposed method achieves to in detection accuracy to realizes the dual tasks of accurate insulator detection and fine counting of insulator tile number, which provides a new solution for the fine detection of insulators in high altitude power transmission lines.

关键词： YOLO Accuracy Power transmission lines Thermal engineering Imaging High-voltage techniques Detectors Insulators detection algorithms Software engineering

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Performance study of the acoustic detection algorithm for the local discharge model

Performance study of the acoustic detection algorithm for th...

引用

IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Qi Yang Deng Song Liuqing Yang Xianyin Mao Huarong Zeng Yao Zhong Meihua Mountain Freezing Climate Disaster and Equipment Safety Observation and Research Station of Guizhou Province Guiyang China Electric Power Research Institute of Guizhou Power Grid Co. Ltd. Guiyang 550002 Guizhou China Guiyang China

ISBN: (数字)9798331523527

ISBN: (纸本)9798331523534

This paper designs a set of acoustic detection system based on high-performance 80-channel multi-arm spiral microphone array based on the application of acoustic imaging technology in local discharge detection of power equipment. Through the combination of theoretical analysis, simulation and experimental verification, DAMAS2 (CBF), DAMAS2 and OMP-DAMS. The results show that although CBF algorithm can achieve basic discharge position positioning, its spatial resolution is relatively poor; DAMAS2 algorithm has improved resolution, but still has low computational efficiency. In contrast, OMP-DAMAS algorithm performs excellent in terms of resolution, positioning accuracy and calculation efficiency. Especially in the context of complex environment, it can more accurately identify the local discharge position, and the calculation speed is fast, which is suitable for real-time detection system. The research results of this paper provide an effective acoustic imaging algorithm and its practical scheme for the accurate detection of local discharge of power equipment, which has high application value and promotion prospect.

关键词： Accuracy Array signal processing Acoustic imaging Acoustic arrays Discharges (electric) Microphone arrays Acoustics Spatial resolution detection algorithms Load modeling

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Application of the MOD-STL-Louvain Method for Community detection in Manufacturing Industries: A Study on Electricity Consumption

Application of the MOD-STL-Louvain Method for Community Dete...

引用

IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN)

作者： Zhiqiang Lan Guoyao Wu Yongjie Guo Fan Pan Yingxin Shen Jiacheng Wu Yanan Yu Yuan He Yujun Yan State Grid Fujian Marketing Service Center (Metering Center and Integrated Capital Center) Fujian China State Grid Fujian Marketing Service Center (Metering Center and Integrated Capital Center) Xiamen Fujian China Fujian Electric Power Supply Service Co. LTD. Xiamen Fujian China School of Mathematical Sciences Xiamen University Xiame Fujian China School of Mathematical Sciences Xiamen University Xiamen Fujian China

ISBN: (数字)9798331523008

ISBN: (纸本)9798331523015

Electricity consumption data captures the usage patterns across various regions, industries, and enterprises, acting as a crucial indicator of economic activity and growth potential. Therefore, analyzing electricity consumption data is essential for understanding economic development. This paper introduces a novel method, MOD-STL-Louvain, for exploring the clustering patterns of enterprises based on their electricity consumption data. The method combines STL decomposition, complex networks, and the Louvain community detection algorithm to classify enterprises into different communities according to their electricity consumption trends. The method was applied to the electricity consumption data of several industries in a province in Eastern China, and the results showed that it can effectively classify enterprises into different communities, which can be characterized by different electricity consumption trends. This research provides a new perspective for understanding the development status of enterprises in their industries and offers a valuable reference for economic work.

关键词： Economics Manufacturing industries Electric potential Electricity Asia Complex networks Market research detection algorithms

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Potato Disease detection Algorithm Based on Improved YOLOv8

Potato Disease Detection Algorithm Based on Improved YOLOv8

引用

Computer, Vision and Intelligent Technology (ICCVIT), International Conference on

作者： Peng Zhou Liqing Geng Genghuang Yang Liming Chen School of Automation and Electrical Engineering Tianjin University of Technology and Education Tianjin China Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin China Fitow (Tianjin) Detection Technology Co.Ltd Tianjin China

ISBN: (数字)9798331540043

ISBN: (纸本)9798331540050

Address the challenges posed by the diverse range of potato fruit defects, which encompass difficulties in detection and limited accuracy, an improved YOLOv8 potato disease detection algorithm is proposed. Firstly, for the lack of spatial information capturing ability of the original YOLOv8 model, the DRB idea is introduced to improve the C2f module, and then the original C2f module is improved by the improved C2f-DRB module. Secondly, for the problem that small targets of potato diseases are not easy to be detected, the CARAFE up-sampling operator is introduced to improve the model's ability of extracting features of the disease, and to enhance the effect of the disease detection. Finally, the original IoU loss function of the model is improved using the Inner IoU loss function, which enhances the generalization ability of the model. Relevant comparison experiments were constructed for ten kinds of diseased potatoes, and the results showed that the improved YOLOv8 algorithm improved 2.4%, 1.7%, and 1.5% in precision, recall, and mAP50, respectively, which proved the proposed algorithm had better performance, and verified the effectiveness and feasibility of the proposed algorithm.

关键词： Computer vision Accuracy Feature extraction detection algorithms Diseases

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