Application of Improved Invariant Moments and SVM in the Recognition of Solar Cell Defects

In this paper, for evaluating the 3-D roughness of the additive manufacturing surfaces, we constructed a 3-D reconstruction system of structured light scanner. By calibrating the system, the center line of structured light is extracted online to reconstruct the profile of additive manufacturing and realize data registration. A dynamic texture coarseness algorithm is proposed, which combines 3-D data with 2-D Gaussian filtering and texture coarseness characteristics to transform 3-D roughness into visual image texture coarseness. The algorithm is applied to evaluate 3-D weld roughness with low delay. The validity of the algorithm is verified by roughness comparison specimens and the actual material adding experiment. The result of roughness is reliable and conforms to the evaluation standard of weld quality. At the same time, the position of structured light is optimized in the process of on-line detection, which reduces the complexity of extracting contour centerline and ensures the low delay characteristic of roughness calculation. INDEX TERMS 3-D reconstruction, dynamic texture coarseness, 3-D roughness of additive manufacturing.

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“…In (9), the k value is determined as follows: texture boundary points, larger and smaller texture primitive interior points.…”

Section: B Dtc Algorithmmentioning

confidence: 99%

Visual Texture-Based 3-D Roughness Measurement for Additive Manufacturing Surfaces

Peng

Zhao

et al. 2019

IEEE Access

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“…Based on crystalline silicon in the photovoltaic (PV) industry, solar cells which act as an alternative renewable energy resource to traditional fossil energy are widely used all over the world in the past few decades [1]. However, due to these uncontrollable factors such as environmental elements and human elements, diverse types and degrees of defects in solar cells such as off-grid and crack are produced possibly in the production, transportation and installation process [2]. These defects not only degrade the power generation efficiency and service lifespan of PV modules, but also lower the prestige of PV farms which bring disastrous loss to the enterprises in economic benefits.…”

Section: Introductionmentioning

confidence: 99%

An intelligent defect detection method in solar cells via IDATLGAN

Pang

2023

3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023)

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Intelligent defect detection is a key section of the PDCA comprehensive quality management in the solar cells production. deep learning-based models for its powerful capacity of extracting features has successfully applied to defect detection tasks. Nevertheless, the deep learning-based models is susceptible to these problems such as insufficient labelled data and class imbalance, which deteriorates the performance of the deep models. What's worse, the new unknown defects without labels in the target domain tend to be recognized as the unknown defects in the source domain. To solve these problems, a novel method named IDATLGAN is proposed. Experimental results show that the proposed model outperforms the DCNN model in feature extraction ability and fulfil the transfer tasks even when faced with new unknown defects without labels.

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Application of Improved Invariant Moments and SVM in the Recognition of Solar Cell Defects

Cited by 2 publications

References 2 publications

Visual Texture-Based 3-D Roughness Measurement for Additive Manufacturing Surfaces

Visual Texture-Based 3-D Roughness Measurement for Additive Manufacturing Surfaces

An intelligent defect detection method in solar cells via IDATLGAN

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