Segmentation technique for the detection of Micro cracks in solar cell using support vector machine

Singh, Om Dev; Gupta, Shailender; Dora, Shirin

doi:10.1007/s11042-023-14509-8

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…Support vector machine and back propagation neural network were used for classification into cracked and non-cracked cells, and the researchers achieved high classification accuracies of 92.67% and 93.67% using SVM and BPNN, respectively. Winston et al [16] also adopted this model, using six input parameters, and both methods showed promising results with average accuracies of 87% and 99%, respectively, and an F1-score of 94.6%, recall of 96.3%, and precision of 87.3% [17].…”

Section: Literature Reviewmentioning

confidence: 99%

Automated Micro-Crack Detection within Photovoltaic Manufacturing Facility via Ground Modelling for a Regularized Convolutional Network

Animashaun,

Hussain

2023

Sensors

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The manufacturing of photovoltaic cells is a complex and intensive process involving the exposure of the cell surface to high temperature differentials and external pressure, which can lead to the development of surface defects, such as micro-cracks. Currently, domain experts manually inspect the cell surface to detect micro-cracks, a process that is subject to human bias, high error rates, fatigue, and labor costs. To overcome the need for domain experts, this research proposes modelling cell surfaces via representative augmentations grounded in production floor conditions. The modelled dataset is then used as input for a custom ‘lightweight’ convolutional neural network architecture for training a robust, noninvasive classifier, essentially presenting an automated micro-crack detector. In addition to data modelling, the proposed architecture is further regularized using several regularization strategies to enhance performance, achieving an overall F1-score of 85%.

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Section: Literature Reviewmentioning

confidence: 99%