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Multi-component attention-based convolution network for color difference recognition with wavelet entropy strategy
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DOI：10.1016/j.aei.2022.101603.png)
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Abstract
En 中文
The recognition of color differences in solar cells with comple x textures is a significant challenge in cel l manufacturing. Traditional methods fai l to detect the color difference effectively. Deep learning models have exhibited promise in many engineering fields. A multi-component attention-based convolution approach is proposed for the surface inspection based on the feature information in different color spaces. Wavelet entropy is employed to represent the information of different components, remove redundant components and extract effective featu r e information. Additionally, a residual attention mechanism is developed to capture local features with contextual semantic information. The best network structure is determined by evaluating the layer depth of the basic model and convolution kernel size. A multi-component network model is constructed based on the formed structu r e to improve the ability to distinguish different color difference features. Experimental results indicated that the proposed approach exhibits competitive performance. The research solution provides guidance for applications of deep learning to improve the quality of solar cells in manufacturing.
Keywords:
Wavelet entropy
Residual attention
Multi-component
Solar cells
Color difference
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