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检索条件"主题词=detection Algorithms"

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Lightweight metal parts detection algorithm based on improved YOLOv11n

Lightweight metal parts detection algorithm based on improve...

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

作者： Lingfeng Huang School of Information Engineering SHANGHAI MARITIME UNIVERSITY Shanghai China

ISBN: (数字)9798331542856

ISBN: (纸本)9798331542863

The progress of science and technology and the development of management concept promote the change of warehouse management to improve efficiency, reduce costs and improve service quality. As a key process, the warehouse picking mode has evolved from manual to mechanization to automation and intelligence. Although automation and intelligence improve the efficiency and accuracy of picking, many medium, small and micro enterprises are still in the stage of mechanization or manual picking. For precision parts manufacturers that cannot use automated equipment, people are optimizing picking paths and using voice technology and augmented reality (AR) technologies to improve efficiency. Augmented reality increases worker wellbeing by increasing speed and reducing error rates, but target recognition is still limited by network latency. In order to reduce the number of model parameters, narrow the model size, and ensure that the model can be successfully deployed on mobile devices with limited computing resources. This paper presents a lightweight metal parts detection algorithm based on improved YOLOv11 to improve the warehouse picking efficiency of small and medium enterprises. By constructing a metal parts data set containing real picking scenes, introducing the LSCD-Head detection head, LAMP pruning method and knowledge distillation, the model is lightweight and maintains high accuracy. The experimental results show that while maintaining the accuracy, the number of parameters and the calculation tation are significantly reduced and the speed is improved, which is better than the traditional YOLOv11n model.

关键词： Performance evaluation Solid modeling Accuracy Automation Computational modeling Metals Manuals Mobile handsets detection algorithms Augmented reality

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Intelligent Malware detection: Enhancing Accuracy with Recurrent Neural Network

Intelligent Malware Detection: Enhancing Accuracy with Recur...

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： C. A. Kandasamy P. Chandru J.K. Kanimozhi E. Loganathan N. Prakash A. Yoganathan Department of Computer Applications K.S.R. College of Engineering Tiruchengode

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Aim: The purpose of this research is to use Artificial Neural Networks (ANN) to create an intelligent malware detection system and evaluate how well it performs in comparison to ClamA V, a conventional antivirus program that relies on signatures. By using Recurrent Neural Networks (RNN) for feature extraction and classification, the study aims to increase detection accuracy. Materials and Methods: Two groups are taken into consideration in this study. Group 1 is the ANN-based intelligent malware detection system, ClamA V which uses with 26 samples. Group 2 is the 26- sample RNN signature-based detection algorithm. With a 95% confidence interval and a threshold of 0.05%, the G Power value is 80%. Both static and dynamic features taken from malware samples are processed by the suggested RNN -based malware detection method. The collection includes labelled To increase convergence, a backpropagation technique with an adaptable learning rate is used to train the ANN model. Results: The ANN- based intelligent malware detection system outperforms the ClamA V system in terms of detection accuracy (0.92 vs. 0.85), precision (0.88 vs. 0.80), recall (0.91 vs. 0.63), and F1-score (0.91 vs. 0.81). While the ClamA V detection accuracy ranges from 55.23% to 78.650/0, the RNN-based system's detection accuracy ranges from 85.42% to 98.76%. At a significance level of 0.0047 and a learning rate of 0.001, the best classification accuracy is obtained. Conclusion: This study shows that the suggested ANN-based malware detection system more especially, the RNN performs noticeably better than the conventional ClamA V system.

关键词： Recurrent neural networks Accuracy Artificial neural networks Feature extraction Market research Malware Distance measurement Pattern recognition Informatics detection algorithms

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A LiDAR Loop Closure detection Algorithm Integrating Multiple Information of Point Cloud

A LiDAR Loop Closure Detection Algorithm Integrating Multipl...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chuan Zhang Qiang Hou Zhengpeng Lu Xuefen Zhu Bonan Jin Tao Zhang School of Instrument Science and Engineering Southeast University Nanjing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

In SLAM (Simultaneous Localization and Mapping) systems, the odometry in the front-end performs inter-frame matching in unstructured environments. However, due to the accumulation of errors, the accuracy of localization and mapping deteriorates. Without external observations, the loop closure optimization strategy triggered by the loop closure detection algorithm is a key method for reducing cumulative errors. Therefore, a LiDAR SLAM loop closure detection algorithm integrating multiple information of point cloud is designed. The algorithm focuses on encoding the height and density information of point cloud. Firstly, a kd-tree-based fast screening of loop closure frames is performed. Then, a refined loop closure frame selection based on similarity calculation is conducted, followed by a geometric consistency verification based on the NICP method. Experimental results on an online unstructured dataset demonstrate that the root mean square error (RMSE) of the absolute trajectory error after optimization by the proposed algorithm is reduced to 0.87 meters, showing a performance improvement of approximately 36%, with point cloud mapping results being more accurate and detailed. Moreover, the precision-recall (PR) curve of the proposed algorithm covers the largest area compared to other loop closure detection algorithms such as Scan Context and Iris.

关键词： Point cloud compression Accuracy Simultaneous localization and mapping Laser radar Search problems Encoding Trajectory Odometry detection algorithms Optimization

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Lightweight Driver and Window detection Algorithm Based on Improved YOLOv5s

Lightweight Driver and Window Detection Algorithm Based on I...

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IEEE International Conference on Industrial Informatics (INDIN)

作者： Yuan Liu ZeXin Xu ChunQi Gao Yong Wang Shandong Academy of Sciences Institute of Automation Qilu University of Technology JiNan China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Aiming at the high complexity of the real-time driver detection model for highway autonomous card acceptance robots, which is difficult to be deployed on limited resource devices, this paper proposes a lightweight driver and window detection algorithm based on YOLOv5s. The algorithm combines the C3P and GS modules of PConv and GhostNet, introduces a parameter-free attention mechanism, extracts multi-scale features from the network, focuses on the target information, and the GS module performs feature fusion with DWConv to replace the feature fusion part in the original YOLOv5s network, and introduces a non-parametric attention mechanism of the PfAAMLayer in the Detect part, improving the ability to generalise *** finally the FGS-pf-YOLOv5s network is constructed by using SIoU (Scale-Invariant Objective for Union) loss to better deal with the occlusion and overlapping problems between targets, which reduces the number of network parameters and the GFLOPs while maintaining the model recognition accuracy, and reduces the number of GFLOPs in the network. number and GFLOPs, reducing the size of the model. The experimental results show that the parameters of the PGS-pf-YOLOv5s model proposed in this paper are reduced by 68%, the GFLOPs are reduced by 51 %, and the size of the weight file is only 4.7 4M, which is reduced by 67% compared with the source code, but the Map50 is improved by 0.2% compared with the source code on the basis of lightweighting, This shows that the model in this paper can solve the model deployment problem while achieving real-time target detection and maintain the same high accuracy.

关键词： YOLO Accuracy Attention mechanisms Target recognition Source coding Feature extraction Real-time systems Robustness detection algorithms Vehicles

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A Joint detection System for Radio Frequency Interference in Spaceborne Microwave Radiometers

A Joint Detection System for Radio Frequency Interference in...

引用

Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Huiyuan Li Chuan Bai Shijie Rong Jiakai He Lifei Jiang Passive Remote Sensing Department Shanghai Aerospace Electronic Technology Institute Shanghai China Active Remote Sensing Department Shanghai Aerospace Electronic Technology Institute Shanghai China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

Microwave radiometer is a highly sensitive receiver for detecting the emitted and scattered energy of objects, but the radio frequency interference (RFI) caused by the use of radio equipment seriously affects the measurement accuracy of the microwave radiometers. In the view of the increasingly serious RFI of microwave radiometers, the joint RFI detection algorithm system has been proposed for spaceborne microwave radiometers. The frequency domain is subdivided in the mode of digital channelized, then the time domain detection and the statistical domain detection are combined in each sub band to identify and suppress interference signals. The time-domain detection algorithm is optimized to determine the specific position and duty cycle of the interference signals, and then, the kurtosis algorithm is used to determine the interference signal so that it can eliminate the bottom noise fluctuations caused by external environmental factors. Finally, a high-resolution fast detection method is achieved and the system simulation performance is provided. The algorithm implementation complexity is low, it is easy to reuse and also suitable for the engineering implementation of RFI detection in satellite microwave radiometers.

关键词： Microwave measurement Interference Microwave devices Kurtosis Systems simulation Microwave radiometry detection algorithms

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Cross Language Similarity detection Technology Combining Optimized K-Means Algorithm with CL-ESA

Cross Language Similarity Detection Technology Combining Opt...

引用

Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Yuxin Gan Qian Xu Foreign Language Department Chengdu College of Arts and Sciences Chengdu China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

Academic plagiarism is becoming an increasingly serious cross language problem, and the academic community is paying more and more attention to cross language text similarity detection algorithms. In order to improve the accuracy of cross linguistic text similarity detection algorithms, the study first combines word level precise translation and sentence level contextual understanding, and combines machine translation algorithms based on word granularity text with those based on sentence granularity text to design an improved machine translation algorithm. At the same time, a heuristic retrieval mechanism is introduced on the basis of CL-ESA to narrow down the search scope of cross linguistic text similarity detection, and an improved index document selection algorithm (IIDSA) is proposed in combination with the K-Means algorithm. Finally, a cross lingual text similarity detection model is constructed by combining the IIDSA algorithm with an improved machine translation algorithm. This study extracted 500 pairs of concept aligned Chinese and English texts from Wikipedia as experimental texts to test the algorithm. The results show that the accuracy of the model is 96.2%, which is superior to other models. The above conclusions verify that the proposed cross language similarity detection model can meet people's needs for cross language text similarity detection and to some extent promote the development of academic research.

关键词： Accuracy Translation Text recognition Plagiarism Heuristic algorithms Clustering algorithms Linguistics Internet Machine translation detection algorithms

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A multi-objective genetic algorithm based on neighborhood coevolution for community detection

A multi-objective genetic algorithm based on neighborhood co...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Mingyuan Bi Junliang Shang Xiaotong Kong Feng Li Yuanyuan Zhang Jin-Xing Liu School of Computer Science Qufu Normal University Rizhao China School of Information and Control Engineering Qingdao University of Technology Qingdao China School of Health and Life Sciences University of Health and Rehabilitation Sciences Qingdao China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Community detection has attracted growing interest, with multi-objective evolutionary algorithms proving to be highly competitive in this area. In this paper, a community detection method based on a multi-objective neighborhood coevolution genetic algorithm, NCMOGA, is proposed. To improve the computational efficiency in large-scale networks, NCMOGA introduces a network processing strategy to simplify the network before and during evolution. A neighborhood coevolution strategy is proposed, in which the corresponding subpopulation is formed according to the neighborhood of each individual. A series of operations such as crossover, mutation and update are performed in the subpopulation, emphasizing the synergy between individuals and their neighbors. Mating selection and crossover operations are performed based on the center selection idea of density peak clustering, and the most important nodes are selected to generate offspring. The effectiveness of NCMOGA is verified on synthetic networks and real-world networks. In addition, the results in guiding the classification of disease and healthy samples demonstrate the high quality of the modules detected by NCMOGA.

关键词： Evolutionary computation Computational efficiency detection algorithms Bioinformatics Genetic algorithms Diseases

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Human detection Based on Learning and Classification of Radio Scattering Parameters and Para-Hermitian Eigenvalue Decomposition

Human Detection Based on Learning and Classification of Radi...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Frank E. Ebong Nicola Novello Andrea M. Tonello Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

In this article, we introduce a novel machine learning algorithm termed the S-parameters-based Human detection Algorithm (S-PBHD). Designed to operate on radio frequency datasets transformed into an S-parameter multi-port channel matrix, our algorithm aims to detect human targets within indoor environments using distributed antennas. Central to the efficacy of this algorithm lies its preprocessing phase, which entails the extraction of critical components from the scattered radio field. This is achieved through the Para-Hermitian Eigenvalue Decomposition method, which effectively disentangles the complexities inherent in the S-parameter multi-port channel matrix.

关键词： Machine learning algorithms Eigenvalues and eigenfunctions Scattering parameters Sensors Indoor environment Complexity theory Classification algorithms Matrix decomposition detection algorithms Land mobile radio

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IAT-YOLO: A black pig detection model for use in low-light environments

IAT-YOLO: A black pig detection model for use in low-light e...

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

作者： Mengzhen Huang Lao Li Huanjun Hu Yifan Liu Yuting Yao Rui Song School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In the large-scale intelligent pig farming industry, it is crucial to achieve real-time and accurate identification and positioning of each pig. However, the existing mainstream pig detection algorithms are mainly designed for white pigs, and the detection effect of black pigs is poor in low-light environments. In addition, when black pigs gather and adhesion and occlusion occur, the complexity of detection is further increased. Although strong light irradiation in the pigpen can improve the detection accuracy, it will have an adverse effect on the normal growth of pigs. To ensure accurate detection of black pigs in low-light environments without interfering with the normal growth of pigs, this research presents the IAT-YOLO model, derived from YOLOv7. It combines the brightness adaptive module IAT (Illumination-Adaptive-Transformer), the CA (Coordinate Attention mechanism) and the improved SPPCSPC-R module, which can effectively identify and locate the position and number of black pigs. Tests on the low-light black pig dataset reveal that the proposed model attains 96.8% accuracy, 83.8% recall, 91.8% mAP@0.5, and 54.5% mAP@0.5:0.95, which demonstrates higher accuracy and provides technical support for the intelligent detection of black pigs in such environments.

关键词： Smart agriculture Adaptation models Radiation effects Accuracy Computational modeling Object detection Robustness Real-time systems detection algorithms Farming

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An Improved Fire detection Algorithm Based on YOLOv5

An Improved Fire Detection Algorithm Based on YOLOv5

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International Conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Zhenjie Wu Xiang Wang Gang Wang Chengzhe Jiang Beihua University Jilin China Jilin Institute of Chemical Technology Jilin China

ISBN: (数字)9798350350760

ISBN: (纸本)9798350350777

Early warning of fire is crucial for improving survival rates during fire incidents. Fire and smoke detection is an extremely important safety technology that plays a vital role in timely and accurately detecting fires in the surrounding environment to ensure the safety of lives and production environments. To address the challenge of detecting fires in complex scenarios, we propose a YOLO-based algorithm, utilizing YOLOv5 as its foundation, which offers high detection accuracy while remaining lightweight. Our algorithm incorporates the C2f-ODConv module into the network, allowing for dynamic adjustment of the convolution kernel's shape and size based on the input data's features during the convolution process. This adaptability ensures better compatibility with different input data types. Additionally, we introduce the Decoupled head in the detection head section, replacing the original detection head. This modification reduces interference between the classification head and the detection head of the model, thereby enhancing the overall detection accuracy of the model. Experimental results demonstrate that our proposed algorithm achieves a detection accuracy of 94.7% and a recall rate of 93.2%. These impressive results ensure accurate Decoupled head fire detection even in complex situations.

关键词： YOLO Adaptation models Accuracy Convolution Heuristic algorithms Interference Feature extraction Safety Kernel detection algorithms

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