Semi-automatic quality inspection of solar cell based on Convolutional Neural Networks

Quality control of solar cells is a very important part of the production process. A little crack or joint failure can cause bad performance of the cell in the future, partly because the defective areas can be electrically disconnected from the active zones. Nowadays, one of the techniques to carry out this control is electroluminescence (EL), which allows obtaining high-resolution images of the cells where a visual and non-invasive inspection of defects can be done. This inspection is mostly performed by trained human operators. However, as the eyes become tired after a working day and the subjectivity of the operators, the accuracy with which the defect detection is done may be compromised. In order to solve this problem, a method to assist the operator in the inspection of polycrystalline silicon solar cells surface from EL images based on Convolutional Neural Networks is proposed. The method would classify the cells as defective and non-defective, and suggest those cells that are defective for re-inspection. Also, it would propose a segmentation map of the defects in the cell. To compensate for the lack of image samples in the dataset, each cell image is divided into regions by a sliding window. Then, each region is classified as defective or non-defective. And finally, all classifications related to the cell are resembled obtaining a segmented image of defective areas in the cell.

[1]  Kun Liu,et al.  Solar cell surface defect inspection based on multispectral convolutional neural network , 2018, Journal of Intelligent Manufacturing.

[2]  Du-Ming Tsai,et al.  Wavelet-based defect detection in solar wafer images with inhomogeneous texture , 2012, Pattern Recognit..

[3]  Mingzhu Wang,et al.  Development and Improvement of Deep Learning Based Automated Defect Detection for Sewer Pipe Inspection Using Faster R-CNN , 2018, EG-ICE.

[4]  Ali Farhadi,et al.  YOLOv3: An Incremental Improvement , 2018, ArXiv.

[5]  Huanlong Zhang,et al.  Solar Cell Surface Defects Detection based on Computer Vision , 2017 .

[6]  G. B. Lush,et al.  Machine vision for solar cell characterization , 2000, Electronic Imaging.

[7]  M. Dhimish,et al.  The impact of cracks on photovoltaic power performance , 2017 .

[8]  Daniel Maestro-Watson,et al.  Deep Learning for Deflectometric Inspection of Specular Surfaces , 2018, SOCO-CISIS-ICEUTE.

[9]  Daniel Philipp,et al.  Why Do PV Modules Fail , 2012 .

[10]  Liu Li,et al.  Rail Surface Defect Detection Method Based on YOLOv3 Deep Learning Networks , 2018, 2018 Chinese Automation Congress (CAC).

[11]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Kaiming He,et al.  Mask R-CNN , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).

[13]  Xinghui Dong,et al.  Small Defect Detection Using Convolutional Neural Network Features and Random Forests , 2018, ECCV Workshops.

[14]  Roberto Pierdicca,et al.  DEEP CONVOLUTIONAL NEURAL NETWORK FOR AUTOMATIC DETECTION OF DAMAGED PHOTOVOLTAIC CELLS , 2018, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences.

[15]  Yao Ming,et al.  Solar Cells Surface Defects Detection Using RPCA Method , 2013 .

[16]  S. Kajari-Schršder,et al.  Criticality of Cracks in PV Modules , 2012 .

[17]  Hichem Snoussi,et al.  A fast and robust convolutional neural network-based defect detection model in product quality control , 2017, The International Journal of Advanced Manufacturing Technology.

[18]  Patrick Rives,et al.  A New Metric for Evaluating Semantic Segmentation: Leveraging Global and Contour Accuracy , 2017, 2018 IEEE Intelligent Vehicles Symposium (IV).

[19]  Christian Riess,et al.  Automatic Classification of Defective Photovoltaic Module Cells in Electroluminescence Images , 2018, Solar Energy.

[20]  Yu-Teng Liang,et al.  Micro crack detection of multi‐crystalline silicon solar wafer using machine vision techniques , 2011 .

[21]  Kincho H. Law,et al.  Detection and Segmentation of Manufacturing Defects with Convolutional Neural Networks and Transfer Learning , 2018, Smart and sustainable manufacturing systems.

[22]  Bin Yang,et al.  Automated Detection of Solar Cell Defects with Deep Learning , 2018, 2018 26th European Signal Processing Conference (EUSIPCO).

[23]  Shivkumar Kalyanaraman,et al.  DeepSolarEye: Power Loss Prediction and Weakly Supervised Soiling Localization via Fully Convolutional Networks for Solar Panels , 2017, 2018 IEEE Winter Conference on Applications of Computer Vision (WACV).

[24]  Du-Ming Tsai,et al.  Defect Detection in Solar Modules Using ICA Basis Images , 2013, IEEE Transactions on Industrial Informatics.

[25]  Mohd Abdullah,et al.  Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique , 2014, EURASIP Journal on Image and Video Processing.

[26]  Christian Riess,et al.  Segmentation of Photovoltaic Module Cells in Electroluminescence Images , 2018, ArXiv.