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| Solar Cells Surface Defects Detection Based on Deep Learning |
| WANG Xian-Bao1,2, LI Jie1, YAO Ming-Hai1, HE Wen-Xiu1, QIAN Yun-Tao2 |
1College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023
2College of Computer Science and Technology, Zhejiang University, Hangzhou 310027 |
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Abstract Defects of solar cells are detected mainly by manual operation, and they are difficult to be detected automatically by traditional charge-coupled device(CCD) imaging system. As a training multi-layer neural network, deep learning draws great attention due to its strong ability to extract features from input sample data. A method for solar cells surface defects detection based on deep learning is proposed. Firstly, deep belief networks(DBN) are established and trained according to the sample features to obtain the initial weights of the networks. Then, the traditional BP algorithm is conducted to fine-tune the network parameters to get the mapping relationship between the training samples and the defect-free template. Finally, the defects of testing samples are detected by the contrast between the reconstruction image and the defect image. Experimental results show that DBN perfectly establishes the mapping relationship, and it can quickly detect defects with a high accuracy.
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Received: 07 January 2014
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2014, Vol. 27 
