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Research on a multi‑dimensional image information fusion algorithm based on NSCT transform

Frontiers of Optoelectronics 2024年第1期17卷 29-44页

作者： Yuxiang Su Xi Liang Danhua Cao Zhenyu Yang Yuanlong Peng Ming Zhao School of Optical and Electronic Information Huazhong University of Science and TechnologyWuhan 430074China State Grid Information and Telecommunication Branch Beijing 100761China

Traditional inspection cameras determine targets and detect defects by capturing images of their light intensity,but in complex environments,the accuracy of inspection may *** based on polarization of light can characterize various features of a material,such as the roughness,texture,and refractive index,thus improving classification and recognition of *** paper uses a method based on noise template threshold matching to denoise and preprocess polarized *** also reports on design of an image fusion algorithm,based on NSCT transform,to fuse light intensity images and polarized *** results show that the fused image improves both subjective and objective evaluation indicators,relative to the source image,and can better preserve edge information and help to improve the accuracy of target *** study provides a reference for the comprehensive application of multi-dimensional optical information in power inspection.

关键词： power inspection Object detection Polarization imaging Image fusion Image denoising

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Online Monitoring Method for Insulator Self-explosion Based on Edge Computing and Deep Learning

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CSEE Journal of power and Energy Systems 2022年第6期8卷 1684-1696页

作者： Baoquan Wei Zhongxin Xie Yande Liu Kaiyun Wen Fangming Deng Pei Zhang State Key Laboratory of Performance Monitoring and Protecting of Rail Transit Infrastructure East China Jiaotong UniversityNanchang 330013China School of Mechatronics and Vehicle Engineering East China Jiaotong UniversityNanchang 330013China School of Electrical Engineering Beijing Jiaotong UniversityBeijing 10091China

Aiming at the problems of traditional centralized cloud computing which occupies large computing resources and creates high latency,this paper proposes a fault detection scheme for insulator self-explosion based on edge computing and DL(deep learning).In order to solve the high amount of computation brought by the deep neural network and meet the limited computing resources at the edge,a lightweight SSD(Single Shot MultiBox Detector)target recognition network is designed at the edge,which adopts the MobileNets network to replace VGG16 network in the original model to reduce redundant *** the cloud,three detection algorithms(Faster-RCNN,Retinanet,YOLOv3)with obvious differences in detection performance are selected to obtain the coordinates and confidence of the insulator self-explosion area,and then the self-explosion fault detection of the overhead transmission line is realized by a novel multimodel fusion *** experimental results show that the proposed scheme can effectively reduce the amount of uploaded data,and the average recognition accuracy of the cloud is 95.75%.In addition,it only increases the power consumption of edge devices by about 25.6W/h in their working *** with the existing online monitoring technology of insulator selfexplosion at home and abroad,the proposed scheme has the advantages of low transmission delay,low communication cost and high diagnostic accuracy,which provides a new idea for online monitoring research of power internet of things equipment.

关键词： Deep learning edge computing insulator self-explosion online monitoring power inspection

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