Research On Image Fusion Algorithm In Fault Detection Of Power Equipment

Power system reform is gradually moving towards intelligence and informatization.The increasing number of power equipment leads to frequent equipment failures,so it is particularly important to ensure the safe and stable operation of power equipment.As the most important information carrier,image is a valuable research topic to realize fault point detection of power equipment by image.Therefore,in order to locate the fault location quickly and accurately,this paper researches the image fusion algorithm in power equipment fault detection.The details are as follows:Firstly,the phenomenon of image offset or rotation is very typical in the process of image acquisition of power equipment.This is a problem that must be solved before image fusion,which requires the image registration technology.The image of power equipment is exceptionally complicated,and the complicated image will generate massive disparate types of feature points.Some of these feature points are counterproductive points,which not only increases the amount of calculation,but also dwindles the accuracy of registration and affects the matching effect,such as low contrast points and edge points.In this paper,the scale invariant feature transformation algorithm is upgraded,and the matching process is divided into sketchy and fine processes.The sketchy matching stage is used to discard edge points and low contrast points,while the fine matching stage is carried out in spatial domain.The geometric position relationship and two-way matching technology are used to eliminate repeated matching point pairs.The experimental results display that the upgraded algorithm can dramatically remove the repeated matching point pairs,and the corresponding calculation amount is extremely diminished.In addition,no matter rotating or shifting images of power equipment,accurate registration can be achieved.Secondly,when image fusion is applied to the fault detection of power equipment,it is necessary to pay attention not only to the infrared characteristic fault points,but also to the information beyond the fault points in the visible image of power equipment,so as to better locate the fault points.However,previous algorithms only pay attention to the infrared targets and ignore the details in the visible image.Therefore,based on the NSCT-PCNN algorithm,this paper proposes a unique weighting mechanism,which combines the direction information features of visible images with the edge features after filtering,and fully extracts the texture details in visible images.Compared with other algorithms,the improved algorithm has straightforward growth in each indicator.Thirdly,due to the influence of regional environment,the image of power equipment collected is prone to multi-focus.However,the issues of detail drop and spectral aliasing are often come across in the handle of multi-focus images by earlier algorithms.In order to figure out this issue,the NSCT-PCNN algorithm is upgraded in this article,and the average gradient and direction information features are weighted to adaptive link strength.Through comparative experimental analysis,the details and textures of the fusion images are clear,and the performance index of fusion is also significantly improved.Fourthly,the traditional algorithm cannot achieve color image fusion and cannot meet the needs of human eye characteristics.In order to achieve the fusion of color power equipment images,this topic uses convolutional neural network to achieve the fusion of color power equipment images,and the reinforcing model depth will lead to the decline of model computing capacity,thus shrinking the ability of feature extraction.In order to deal with the above issues,this paper revamps the residual network and constructs a multi-focus image fusion model of power equipment in HSI space.The simulation results display that the design can productively fuse the color images of power equipment,and the fusion results are essentially consistent with the mark images.