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3rd International Conference on High Performance Computing and Communication Engineering, HPCCE 2023

作者： Xie, Wu Su, Zhenzhao Zhang, Huimin Kang, Ping Qin, Kun Fan, Yong Zhao, Quanyou Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541214 China Key Laboratory of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin541004 China Lingchuan Science and Technology Information Institute Guilin541299 China Guilin Hivision Technology Co. Ltd. Guilin541004 China School of Mechanical and Electrical Engineering Guilin University of Electronic Technology Guilin541214 China School of Computer Science and Engineering Guilin University of Aerospace Technology Guilin541004 China

ISBN: (数字)9781510674653

ISBN: (纸本)9781510674646

The detection methods of yellow dragon disease spread via wood lice transmission networks like social networks are very important for diverse citrus trees and farmers. Although current methods have some detection accuracy or low cost, the detection processes are relatively troublesome and the detection cycles are long, making it be a difficult problem to apply them in large-scale orange farms as practical scenarios to detect citrus yellow dragon disease in a timely manner. A new method toward detecting citrus yellow dragon disease spread utilizing Spark and deep learning is proposed for this problem. By obtaining citrus field video stream data through high-definition cameras, and transferring the stream data to the Spark cluster through Kafka like intelligent agents, it is practicable to use the structured streaming component under the Spark framework via big data ecosphere to process video or image stream data transmitted via monitoring. We construct a citrus yellow dragon disease detection model via YOLOv7, and use self-made citrus yellow dragon disease images as training and testing data sets. The preliminary experimental results show that the new methods achieved an accuracy of 83.14%. To reduce the occurrence of missed and false detections, the shallow detection heads are added to the feature fusion networks for improvement, extracting and fusing shallow network information to try to improve the detection effects of yellow dragon disease. Then replace the convolution operation in the ELAN (Effective Long-range Aggregation Network) module with deep separable convolution to reduce the number of model parameters. The preliminary experimental results show that compared to the original YOLOv7 model, our improved citrus yellow dragon disease detection model with YOLOv7 has an accuracy improvement of 2.43%, maintaining a higher detection accuracy with lower time than before. © 2024 SPIE.

关键词： Deep learning

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Efficient Construction of Delaunay Triangulations on Riemannian Manifolds with the Bowyer-Watson Method

Efficient Construction of Delaunay Triangulations on Riemann...

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International Conference on Digital Home (ICDH)

作者： Dejiangmo Qian Wang Li Zhou Xinyun Zhang Dawei Luo Northwest Minzu University Colleage of Mathematics and Computer Science Lanzhou China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331509873

ISBN: (纸本)9798331509880

This paper explores the application of the Bowyer-Watson algorithm for constructing Delaunay triangulations on Riemannian manifolds, with a particular focus on karst terrain and channel detection scenarios. We define geodesic disks with sufficiently small radii and demonstrate the existence of a net within these disks that allows for the construction of triangles satisfying the Delaunay condition. By analyzing the insertion of points both inside and outside of triangles, we ensure proper point placement and perform edge flips as necessary to maintain the Delaunay property. Compared to other algorithms, the Bowyer-Watson approach offers notable advantages for achieving Delau-nay triangulation on Riemannian manifolds due to its flexibility and locality. This method effectively manages complex geometric structures by adaptively adjusting the triangulation, ensuring both accuracy and efficiency in non-Euclidean spaces. It is particularly well-suited for modeling karst terrain features and detecting channels, providing a crucial theoretical foundation and practical guidance for future numerical computations and geometric modeling on curved surfaces.

关键词： Manifolds graphics Accuracy Data analysis Scientific computing image edge detection Geometric modeling Focusing Feature extraction Numerical models

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Chain Recurrent Point and Devaney Chaos of Topological Groups

Chain Recurrent Point and Devaney Chaos of Topological Group...

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International Conference on Smart City and Systems Engineering

作者： JI Zhanjiang QIN Guijiang ZHAI Cong Guangxi Colleges and Universities Key Laboratory of Image Processing and Intelligent Information System Wuzhou University China School of Transportation and Civil Engineering and Architecture Fo Shan University China

ISBN: (纸本)9781728113678;9781728113661

Chain recurrent point, strong chain recurrent point and Devaney chaos have an important significance in terms of theory and application. In this paper, we will introduce the concept of chain recurrent point, strong chain recurrent point and Devaney chaos. By means of properties of the product space and topological conjugation, We study the dynamical properties of chain recurrent point, strong chain recurrent point and Devaney chaos. It is gived that the following conclusions. (1) The set CR(G) and SCR(G) are a closed set. (2) The set CR(G) and SCR(G) are strongly invariable for G. (3) If G_1 and G_2 are to be topologically conjugate, then the group G_1 is Devaney chaotic if and only if the group G_2 is Devaney chaotic;(4) The group G_1 × G_2 is Devaney chaotic if and only if the group G_1 and G_2 are Devaney chaotic. These results enrich the theory of chain recurrent point, strong chain recurrent point and Devaney chaos under the action of topological group

关键词： Chaos Extraterrestrial measurements Smart cities Systems engineering and theory Data science Software engineering

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A Novel Group-based Swarm Optimizer for Large-Scale Optimization

A Novel Group-based Swarm Optimizer for Large-Scale Optimiza...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Shanwen Guan Rushi Lan Yijie Zhu Ruomei Wang Guangxi Key Laboratory of Image and Graphic intelligent processing Guilin University of Electronic Technology Guilin China School of Information and Communication Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728142487

ISBN: (纸本)9781728142494

In this paper, a novel group-based swarm optimizer (GSO) is proposed for large scale optimization. Inspired from the group learning behavior in human society, the hierarchical learning are involved in GSO. In the mass learning stage, GSO randomly picks three particles to form a study group and then adopts the competitive mechanism to update the members of studing group. In the hierarchical learning phase, elite particles, the better fitness value, are directly retained for the next iteration. Worst particles learn two dominant particles to further search for more promising areas in the team at the same time. Another particles utilize differential evolution to explore search space. Then, theoretically and empirically analysis of GSO are presented and the exploration abilities of proposed method are performed compared the most popular particle swarm optimizer. Further, a set of experiments on two widely used large-scale benchmark sets demonstrate that GSO achieves better performance on large scale problems than several state-of-the-art algorithms.

关键词： Optimization Convergence graphics Space exploration Sociology Statistics Next generation networking

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PLM-YOLOv5: Improved YOLOv5 for Pig Detection in Livestock Monitoring

PLM-YOLOv5: Improved YOLOv5 for Pig Detection in Livestock M...

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intelligent Robotics and Automatic Control (IRAC), International Conference on

作者： Ni Peng Fang Li Xiaonan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

With the rapid development of smart agriculture, the demand for pig monitoring has been increasing, particularly in disease prevention and behavioral analysis. Real-time monitoring of pig behavior is essential for improving farming efficiency and ensuring animal welfare. However, challenges such as pigs’ varying body sizes, movement, and complex environments make accurate detection and tracking difficult. To address these issues, this paper proposes an improved YOLOv5 model, named PLM-YOLOv5, specifically designed for pig detection in livestock monitoring. First, we add an additional detection head to enhance the model’s capability to detect smaller pigs effectively. Next, we integrate the Convolutional Block Attention Module (CBAM) to enhance feature extraction, particularly in handling complex backgrounds. Finally, we utilize a self-collected dataset of over 1,400 images for model training and validation, ensuring the model adapts to real-world livestock environments. Experimental results show that PLM-YOLOv5 achieves significant improvements in pig detection tasks, with the mean Average Precision (mAP) increasing by approximately 9% compared to the original YOLOv5. Additionally, training and validation loss curves demonstrate the model's stability and convergence. These results confirm the effectiveness and reliability of PLM-YOLOv5 for real-world applications, offering a robust solution for smart agriculture.

关键词： YOLO Training Smart agriculture Adaptation models Accuracy Head Tracking Real-time systems Livestock Monitoring

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Arbitrary shape text detection method based on improved PSE network structure

Arbitrary shape text detection method based on improved PSE ...

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IEEE International Conference of Safety Produce Informatization (IICSPI)

作者： Chen Xie guangxi Chen Min Tang Qiaochu Li Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728177380

ISBN: (纸本)9781728177397

The problem of low efficiency of text detection in natural scenes due to the Curved text or the multi-directional text, a arbitrary shape text detection method based on PSE network structure is proposed. By changing the backbone network of the original PSE and adopting a new lightweight network as backbone, and the post-processing method of the original network is optimized, which effectively improves the speed without losing the original accuracy. The experimental results show that the speed of the proposed method is 5.81 FPS/s compared with the original PSE on CTW1500 data set. The improved network has good robustness and generalization ability, and has better overall performance.

关键词： Shape Conferences Production Robustness

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Focusing on diacritics to improve Vietnamese scene text detection

Focusing on diacritics to improve Vietnamese scene text dete...

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International Joint Conference on Neural Networks (IJCNN)

作者： Wenhui Huang Shaoliang Shi Yimin Wen Guilin University of Electronic Technology Guilin China China Academy of Science and Technology Development GuangXi Branch Nanning China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Scene text detection is usually recognized as one of the fundamental challenges in computer vision and robotics. While the detection of scene text for English and Chinese has achieved great progress, Vietnamese scene text detection is still a relatively new field. In Vietnamese scene text detection, the diacritics of Vietnamese characters are often too small to be detected, making it difficult to accurately extract their features. The bounding boxes are usually placed close to Latin letters, resulting in incomplete covering for diacritics. To overcome these challenges, we propose a method of focusing on diacritics to detect Vietnamese scene text. Firstly, we propose detailed information embedding to enhance the features of diacritics. Secondly, we introduce IoU-v to allow the proposals to match the boundary of Vietnamese text instances more accurately. Lastly, in mask branch, we incorporate a diacritics feature fusion module to further enhance the features of diacritics. The experimental results illustrate the proposed method outperforms the existing approaches. The code of the proposed algorithm is available at https://***/mlmmwym/FD2VSTD.

关键词： Computer vision Codes Text recognition Neural networks Text detection Focusing Feature extraction

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Text Detection And Recognition Algorithm For Arbitrary Shapes In Natural Scenes

Text Detection And Recognition Algorithm For Arbitrary Shape...

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Frontiers of Electronics, Information and Computation Technologies (ICFEICT), International Conference on

作者： Zhenrong Deng Zongyang Liu Rui Yang Rong Huang Rushi Lan Guangxi Key Laboratory of Graphics Intelligent Processing Guilin University of Electronic Technology Guilin China Nanning Research Institute Guilin University of Electronic Technology Guilin China Guangxi Jinghang UAV Co. Ltd. Nanning China

Natural scenes are rich and varied in text presentation and complex in the background. These characteristics are likely to cause problems such as missing text and difficulties in coordinate regression during detection. At the same time, the gradient disappears due to the long text sequence in the recognition stage, thus causing poor correlation before and after the text. In order to address the issue, this research suggests an end-to-end scene text detection and identification model that emphasizes spatial image data and long-range text relevance. The model uses the ABCNet network as the underlying structure. In the detection stage, this paper uses ResNet to extract image features, feature fusion with iterative FPN, convolution using convolutional blocks with coordinate information, and finally, focus the coordinate information by coordinated attention mechanism. This paper uses a combination of Transformer and LSTM coding blocks in the recognition phase to encode the detection region. It uses Transformer to obtain textual long-sequence relations, LSTM to enhance short-range information, and a lightweight attention mechanism to decode. The experimental findings demonstrate that the suggested model outperforms more widely used approaches in terms of text localisation and recognition.

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Multi-Wavelength Narrow Linewidth Random Fiber Laser Based on Fiber Bragg Grating Fabry-Perot Filter

Multi-Wavelength Narrow Linewidth Random Fiber Laser Based o...

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International Conference on Information Science and Technology (ICIST)

作者： Sokliep Pheng Luo Xiaonan Zhongshuai Wang Yijie Zhu Zetao Jiang School of Information and Communication Guilin University of Electronic Technology Guilin China Guangxi Key Laboratory of Intelligent Processing of Computer Image and Graphics Guilin University of Electronic Technology Guilin China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

ISBN: (数字)9781728155586

ISBN: (纸本)9781728155593

Recently, multi-wavelength narrow linewidth random fiber laser has very interested for every researcher in this field, because of their useful advantages application, such as high-resolution spectroscopy and fiber optic sensing. In this paper, the standard single-mode fiber is used to form a half-opened cavity structure for generating the narrow linewidth random fiber laser and used the FBG-FP as a filter to form the narrow linewidth RFL into multi-wavelength. Firstly, we used the Rayleigh scattering that processed as well as in a standard single-mode fiber to provide random distribution feedback at the same time while using erbium-doped fiber (EDF) to provide the gain or amplification for achieving a broadband random laser output. Then, FBG-FP is added to the half-open cavity random laser structure. The multi-wavelength and narrow linewidth RFL can be achieved when the broadband RFL goes through the FBG-FP. In this paper we have generated the multi-wavelength narrow linewidth random fiber laser which has more than 10 wavelengths and the 3dB bandwidth is less than 0.01 nm and the mode separation of each wavelength is 0.04nm.

关键词： Fiber gratings Erbium-doped fiber lasers Optical fiber couplers Optical fiber amplifiers

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Clglf: Confidence Learning Guides Label Fusion for Multimodal Named Entity Recognition Method

SSRN

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SSRN 2024年

作者： Wang, Hairong Wang, Tong Wang, Yiyan Chen, Fangping School of Computer Science and Engineering North Minzu University Yinchuan750021 China Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission North Minzu University Yinchuan750021 China

A B S T R A C TTo solve the visual semantic understanding bias and multimodal semantic bias in multimodal named entity recognition, the Confidence Learning Guides Label Fusion for Multimodal Named Entity Recognition (CLGLF) method is proposed. This method invokes the BLIP-2 pre-trained model to generate image captions, concatenates them with the input texts, and performs joint coding to achieve multimodal feature fusion. The candidate labels and text labels are obtained after decoding the multimodal representations and text representations. Based on using the KL divergence loss function to align the two groups of labels, the confidence score is calculated to evaluate the quality of the multimodal representation, and a confidence threshold is set to help screen out the biased candidate labels, the text labels in the corresponding positions are used to replace the biased candidate labels, to achieve the label fusion, and finally complete the multimodal named entity recognition. In order to verify the proposed method, experiments are carried out on the Twitter-2015 and Twitter-2017 multimodal datasets, and the experimental results are compared with 7 mainstream methods, such as MSB and UMT. The experimental results show the effectiveness of the CLGLF method. © 2024, The Authors. All rights reserved.

关键词： Semantics

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