Recognition of Power Equipment Based on Multitask Sparse Representation

Lei Lei,Liang Wang,Xinyi Zhang,Hao Wan,Shuhai Zheng,Yanfei Wang,Jian Wu,Bai Yuan Ding

doi:10.1155/2021/8322361

Abstract

Image analysis of power equipment has important practical significance for power-line inspection and maintenance. This paper proposes an image recognition method for power equipment based on multitask sparse representation. In the feature extraction stage, based on the two-dimensional (2D) random projection algorithm, multiple projection matrices are constructed to obtain the multilevel features of the image. In the classification process, considering that the image acquisition process will inevitably be affected by factors such as light conditions and noise interference, the proposed method uses the multitask compressive sensing algorithm (MtCS) to jointly represent multiple feature vectors to improve the accuracy and robustness of reconstruction. In the experiment, the images of three types of typical power equipment of insulators, transformers, and circuit breakers are classified. The correct recognition rate of the proposed method reaches 94.32%. In addition, the proposed method can maintain strong robustness under the conditions of noise interference and partial occlusion, which further verifies its effectiveness.

Highlights

With the continuous increase of power equipment, traditional manual-line inspection and substation monitoring have been difficult to meet the actual requirements
A large number of power-line inspection equipment types based on helicopters, drones, and other computational platforms have been put into application [1,2,3,4,5]. ese devices collect images of power equipment through optical and infrared sensors on themselves or nearby. en, the image analysis and other technical means can be used to determine possible faults in the power equipment. erefore, it is of great significance to carry out the analysis and interpretation of power equipment images
This paper selects several types of existing relevant methods to conduct experiments at the same time, including the method based on the region moments in [11], the method using sparse representationbased classification (SRC) in [5], the method based on support vector machines (SVMs) in [8], and the method using convolutional neural network (CNN) in [21]

Summary

Introduction

With the continuous increase of power equipment, traditional manual-line inspection and substation monitoring have been difficult to meet the actual requirements. In this context, a large number of power-line inspection equipment types based on helicopters, drones, and other computational platforms have been put into application [1,2,3,4,5]. E basic idea is to classify the collected power equipment on the basis of the existing database, so as to provide a prerequisite for the targeted analysis of the special type of equipment. The power equipment image recognition problem is similar to the traditional image-based target recognition problem and basically uses two stages of feature extraction and classifiers. The deep learning models represented by the convolutional neural network (CNN) have become a powerful tool in the field of image

Methods

Findings

Discussion

Conclusion