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
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.
王宪保，李洁，姚明海，何文秀，钱沄涛. 基于深度学习的太阳能电池片表面缺陷检测方法*[J]. 模式识别与人工智能, 2014, 27(6): 517-523.
WANG Xian-Bao, LI Jie, YAO Ming-Hai, HE Wen-Xiu, QIAN Yun-Tao. Solar Cells Surface Defects Detection Based on Deep Learning. , 2014, 27(6): 517-523.
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2014, Vol. 27 
