Deep Learning-Based Model for Defect Detection and Localization on Photovoltaic Panels  

作　　者：S.Prabhakaran R.Annie Uthra J.Preetharoselyn 

机构地区：[1]Department of Computational Intelligence,SRM Institute of Science and Technology,Chengalpattu,603203,India [2]Department of Electrical Engineering,SRM Institute of Science and Technology,Chengalpattu,603203,India

出　　处：《Computer Systems Science & Engineering》2023年第3期2683-2700,共18页计算机系统科学与工程（英文）

摘　　要：The Problem of Photovoltaic(PV)defects detection and classification has been well studied.Several techniques exist in identifying the defects and localizing them in PV panels that use various features,but suffer to achieve higher performance.An efficient Real-Time Multi Variant Deep learning Model(RMVDM)is presented in this article to handle this issue.The method considers different defects like a spotlight,crack,dust,and micro-cracks to detect the defects as well as loca-lizes the defects.The image data set given has been preprocessed by applying the Region-Based Histogram Approximation(RHA)algorithm.The preprocessed images are applied with Gray Scale Quantization Algorithm(GSQA)to extract the features.Extracted features are trained with a Multi Variant Deep learning model where the model trained with a number of layers belongs to different classes of neurons.Each class neuron has been designed to measure Defect Class Support(DCS).At the test phase,the input image has been applied with different operations,and the features extracted passed through the model trained.The output layer returns a number of DCS values using which the method identifies the class of defect and localizes the defect in the image.Further,the method uses the Higher-Order Texture Localization(HOTL)technique in localizing the defect.The pro-posed model produces efficient results with around 97%in defect detection and localization with higher accuracy and less time complexity.

关 键 词：Photovoltaic systems deep learning defect detection CLASSIFICATION LOCALIZATION 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]