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IJLAI Transactions on Science and Engineering 2024年第1期2卷 38-45页

作者： Yongqi Fan Li Hang Botong Sun Software College of Shenyang Normal University No.253Huanghe North StreetHuanggu DistrictShenyang 110034 China College of Journalism and Communication Shenyang Normal UniversityNo.253Huanghe North StreetHuanggu DistrictShenyang 110034 China

In pedestrian re-recognition,the traditional pedestrian re-recognition method will be affected by the changes of background,veil,clothing and so on,which will make the recognition effect *** order to reduce the impact of background,veil,clothing and other changes on the recognition effect,this paper proposes a pedestrian re-recognition method based on the cycle-consistent generative adversarial network and multifeature *** comparing the measured distance between two pedestrians,pedestrian re-recognition is ***,this paper uses Cycle GAN to transform and expand the data set,so as to reduce the influence of pedestrian posture changes as much as *** method consists of two branches:global feature extraction and local feature *** the global feature and local feature are *** fused features are used for comparison measurement learning,and the similarity scores are calculated to sort the samples.A large number of experimental results on large data sets CUHK03 and VIPER show that this new method reduces the influence of background,veil,clothing and other changes on the recognition effect.

关键词： Pedestrian re-recognition Cycle-consistent generative adversarial network multifeature fusion Global feature extraction Local feature extraction

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Mask R-CNN and multifeature clustering model for catenary insulator recognition and defect detection

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2022年第9期23卷 745-756页

作者： Ping TAN Xu-feng LI Jin DING Zhi-sheng CUI Ji-en MA Yue-lan SUN Bing-qiang HUANG You-tong FANG School of Automation and Electrical Engineering Zhejiang University of Science and TechnologyHangzhou 310023China College of Electrical Engineering Zhejiang UniversityHangzhou 310027China

Rod insulators are vital parts of the catenary of high speed railways(HSRs).There are many different catenary insulators,and the background of the insulator image is *** is difficult to recognise insulators and detect defects *** this paper,we propose a catenary intelligent defect detection algorithm based on Mask region-convolutional neural network(R-CNN)and an image processing *** projection technology is used to achieve single shed positioning and precise cutting of the ***,texture,and gray feature fusion(GTGFF)and a K-means clustering analysis model(KCAM)are proposed to detect broken insulators,dirt,foreign bodies,and *** this model,insulator recognition and defect detection can achieve a high recall rate and accuracy,and generalized defect *** algorithm is tested and verified on a dataset of realistic insulator images,and the accuracy and reliability of the algorithm satisfy current requirements for HSR catenary automatic inspection and intelligent maintenance.

关键词： High speed railway(HSR)catenary insulator Mask region-convolutional neural network(R-CNN) multifeature fusion K-means clustering analysis model(KCAM) Defect detection

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