2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)最新文献

Exponentially Smooth Rocket Mode Switching Algorithm Simulation 指数平滑火箭模式切换算法仿真

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00042

Ying Zhang, Dongqiang Shi, Di Peng, Tiantian Li, Qian Zhao

引用次数: 0

Design and Implementation of CNC Sculpture Control System Based on NURBS Interpolation Algorithm 基于NURBS插值算法的数控雕刻控制系统的设计与实现

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00026

Xiaobo Yu

{"title":"Design and Implementation of CNC Sculpture Control System Based on NURBS Interpolation Algorithm","authors":"Xiaobo Yu","doi":"10.1109/prmvia58252.2023.00026","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00026","url":null,"abstract":"Artificial intelligence is the crystallization of human intelligence and an important transformation of human labor tools. In the future development process, artificial intelligence will become a new and high-quality tool, which will bring great value to mankind. The engraving machine numerical control (CNC) system is an automatic tracking system, which has strong practical value. According to the characteristics of NURBS interpolation algorithm, this paper designs a CNC sculpture control system based on NURBS interpolation algorithm, and improves its dynamic characteristics and positioning accuracy. Through the system performance test of the CNC sculptor designed in this paper, starting from the stability and safety, we can see from the experimental data that the CNC sculptor designed in this paper has high stability and safety. In addition, compared with the time spent by the market engraving machine and the CNC engraving machine designed in this paper, it takes 22 minutes and 20 minutes for the market engraving machine and the CNC engraving machine designed in this paper to carve one wooden plate respectively. To sum up, the CNC sculpture control system designed in this paper has a good effect and is worthy of further promotion and application.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123485316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ReM-YOLO: A New Lightweight Vehicle Parts Target Detection Algorithm remm - yolo:一种新型轻量化汽车零部件目标检测算法

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00022

T. Yu, Lei Li, Xunlian Luo, Qiang Li

{"title":"ReM-YOLO: A New Lightweight Vehicle Parts Target Detection Algorithm","authors":"T. Yu, Lei Li, Xunlian Luo, Qiang Li","doi":"10.1109/prmvia58252.2023.00022","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00022","url":null,"abstract":"In the scene of equipment maintenance, the equipment parts target detection technology can provide technical support for maintenance personnel, and lightweight algorithms based on deep learning have been much concerned, which have the advantages of strong feature extraction and short delay time. YOLOv7 is considered as a new algorithm in the YOLO series, which offers many optimized modules to improve target detection abilities. However, YOLOv7 has problems such as huge amount of computation and parameters, serious memory consumption, and the over-optimized structure. In this paper, a lightweight algorithm ReM-YOLO based on YOLOv7 is proposed to improve the network structure. YOLOv7 is improved by adding C3 blocks, MobileOne blocks and Rep-DSC blocks to reduce the model size while maintaining high precision, and a non-parameter SimAM attention module is employed to further improve the detection accuracy. Compared to YOLOv7, the ReM-YOLO has better improvements in precision and recall, and the model size is reduced by 1/3 size of YOLOv7. It has been observed that experimental tests are carried out on our dataset of vehicle engine components with the high accuracy rate of 96.2%. The improved algorithm helps further experiments about model compression effectively.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116314302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Zero-Shot Learning based on Vision Transformer 基于视觉转换器的零射击学习

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00010

Ruisheng Ran, Qianwei Hu, Tianyu Gao, Shuhong Dong

引用次数: 0

Partial Discharge Pattern Recognition in GIS Based on Multiscale Dispersion Entropy and Stacking Ensemble Learning 基于多尺度色散熵和叠加集成学习的GIS局部放电模式识别

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00020

Jingjie Yang, Xiang Zheng

{"title":"Partial Discharge Pattern Recognition in GIS Based on Multiscale Dispersion Entropy and Stacking Ensemble Learning","authors":"Jingjie Yang, Xiang Zheng","doi":"10.1109/PRMVIA58252.2023.00020","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00020","url":null,"abstract":"Partial discharge (PD) monitor in gas-insulated switchgear (GIS) is an important means to detect insulation defects of equipment. To solve the problem that the traditional PD extraction features are not obvious and recognition precision is limited. The paper presents a new pattern recognition algorithm by combining the multiscale dispersion entropy (MDE), locally linear embedding (LLE), and stacking ensemble learning, to effectively refine the recognition correct rate of PD types. First, the MDE values of PD signal were calculated as the feature value. Then, use LLE to reduce dimensions to refine the speed and precision of model recognition. Finally, use stacking ensemble learning to train and recognize the feature values after dimension reduction. Among them, K-nearest neighbor, random forest and Gaussian Bayes were selected for the first layer learners, and logical regression model was selected for the second layer learner. The validation results indicated that the recognition correct rate of the proposed algorithm for four typical PD types in GIS was more than 98%, and it has a strong anti-interference ability, which is significantly better than the traditional feature extraction methods.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"231 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125702221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-shot Classification Network Based on Feature Differentiation 基于特征区分的少样本分类网络

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00047

Jing Chen, Guan Yang, Xiaoming Liu, Yang Liu, Weifu Chen

引用次数: 0

Stereo Image Analysis by Octonion Fractional-Order Orthogonal Color Moments 基于八元分数阶正交色矩的立体图像分析

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00039

C. Peng, Bing He, Wenqiang Xi, Guancheng Lin

{"title":"Stereo Image Analysis by Octonion Fractional-Order Orthogonal Color Moments","authors":"C. Peng, Bing He, Wenqiang Xi, Guancheng Lin","doi":"10.1109/PRMVIA58252.2023.00039","DOIUrl":"https://doi.org/10.1109/PRMVIA58252.2023.00039","url":null,"abstract":"Polar harmonic Fourier moments (PHFMs) are popular for image analysis due to their properties of lower computation complexity and minimal redundant description capability of images. However, the traditional PHFMs are unavailable for color stereo image analysis on the one hand, and on the other hand the polar harmonic polynomials with integer-order are not able to extract fine features. In this paper, a new category of moments named octonion fractional-order PHFMs (OFrPHFMs) are proposed using the fractional-order basis functions of PHFMs and octonion theory. The proposed moments can be viewed as a generalization of quaternion orthogonal moments. Furthermore, since the image moments formed by the octonion descriptor can treat the color stereo image integrally, it has a strong representation capability. More importantly, some numerical instability and calculation issues are discussed and a fast computational framework using matrix operation and block Gaussian numerical integration is developed to improve the accuracy and efficiency of the proposed OFrPHFMs. Finally, to demonstrate the validation of the proposed moments, the image experiments are conducted and the results show that the proposed OFrPHFMs have favorable performance in the field of color stereo image analysis.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"1 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131575001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Low Contrast Feature Extraction and Registration Effect of Concrete Structure based on SuperGlue Algorithm 基于强力胶算法的混凝土结构低对比度特征提取及配准效果研究

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00044

Guojun Wang, Zhenzhen Li, Jianbin Yao

{"title":"Research on Low Contrast Feature Extraction and Registration Effect of Concrete Structure based on SuperGlue Algorithm","authors":"Guojun Wang, Zhenzhen Li, Jianbin Yao","doi":"10.1109/prmvia58252.2023.00044","DOIUrl":"https://doi.org/10.1109/prmvia58252.2023.00044","url":null,"abstract":"In the aspect of low-contrast feature extraction and registration of concrete structure surface, traditional algorithms have some problems such as low computational efficiency, less feature extraction and low matching accuracy. The method based on deep learning has become a mainstream method at present, but the supervised learning method based on manual annotation has the problem that low contrast features cannot be marked. In view of this, it is necessary to study the most promising deep learning method based on graph convolution for progressive extraction and registration of low-contrast features of concrete structure surfaces. This paper uses Superpoint framework to solve the low contrast problem at the end of supervised learning. The indoor and outdoor test results show that the deflection curve trend of measuring points is basically consistent with that of the displacement meter, which indicates the robustness of feature point tracking based on SuperGlue, and further indicates that the method can be used as an effective technical reserve for deflection measurement of concrete structures.","PeriodicalId":221346,"journal":{"name":"2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127580392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Performance Analysis of CHAID Algorithm for Accuracy CHAID算法精度性能分析

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/PRMVIA58252.2023.00036

Chuancheng Deng, Feng Yi, Xing Li, Junyi Tang, Guang Sun

引用次数: 0

Sigma-UAP: An Invisible Semi-Universal Adversarial Attack Against Deep Neural Networks Sigma-UAP:一种针对深度神经网络的隐形半通用对抗性攻击

2023 International Conference on Pattern Recognition, Machine Vision and Intelligent Algorithms (PRMVIA) Pub Date : 2023-03-01 DOI: 10.1109/prmvia58252.2023.00012

Feiyang Qin, Wenqi Na, Song Gao, Shaowen Yao

引用次数: 0