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Detection of Cracks in Solar Panel Images Using Improved AlexNet Classification Method

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Abstract

Renewable energy resources are the only solution to the energy crisis over the world. Production of energy by the solar panel cells are identified as the main renewable energy resources. The generation of energy by the solar panels is affected by the cracks on it. Hence, the detection of cracks is important to increase the energy levels produced by the solar cells. In this paper, the solar panel images are classified into either cracked image or non-cracked image using deep learning algorithm. The proposed method is designed with the following modules preprocessing, enhancement, feature computations, classification and crack segmentation. The source solar panel image are denoised using adaptive median filter as the preprocessing process and then the pixels in denoised solar panel image are enhanced using cumulative enhancement (CE) method. The external features are computed from the CE enhanced solar panel image and these features are classified by Improved AlexNet (IAN)-deep learning classifier to produce the classification results as either cracked or non-cracked solar panel image. Finally, the cracks in classified cracked solar panel image are segmented using morphological algorithm. The main significance of this paper is that the proposed methods stated here detect single and multiple level of minor cracks, which enhances the energy levels production.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, R.M.K. Engineering College, Tiruvallur District, Kavaraipettai, India

    M. Perarasi

  2. Department of Electrical and Electronics Engineering, R.M.K. Engineering College, Tiruvallur District, Kavaraipettai, India

    Geetha Ramadas

Authors
  1. M. Perarasi
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  2. Geetha Ramadas
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Correspondence to M. Perarasi.

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Perarasi, M., Ramadas, G. Detection of Cracks in Solar Panel Images Using Improved AlexNet Classification Method. Russ J Nondestruct Test 59, 251–263 (2023). https://doi.org/10.1134/S1061830922100230

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  • DOI: https://doi.org/10.1134/S1061830922100230

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