基于机器视觉的太阳能电池片表面缺陷检测研究现状及展望  

Research Development and Prospect of Solar Cells Surface Defects Detection Based on Machine Vision

作　　者：钱晓亮[1] 张鹤庆 陈永信 曾黎[1] 刁智华[1] 刘玉翠[1] 杨存祥[1] 

机构地区：[1]郑州轻工业学院电气信息工程学院,郑州450002 [2]许继集团有限公司,河南许昌461000

出　　处：《北京工业大学学报》2017年第1期76-85,共10页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61501407);河南省高等学校重点科研项目(15A413006);河南省科技厅重点科技攻关项目(132102110150)

摘　　要：鉴于基于机器视觉的太阳能电池片表面缺陷检测方法具有操作简便、检测精度高的优势,对此类方法所涉及的各个环节进行了综述.首先,对太阳能电池片表面的各种成像方式和常见缺陷类型进行了归纳总结;其次,对现有的检测方法按照数学建模思路的不同进行了分类介绍和对比分析;最后,对内容进行了小结并对太阳能电池片表面缺陷检测方法的后续研究进行了展望.可以看出:基于机器视觉的太阳能电池片表面缺陷检测方法已经取得了较大的发展,但在特征提取算法设计方面仍有改进空间,如基于深度神经网络的特征提取算法.Considering the advantages of simple operation and high detecting accuracy,all aspects involved in solar cell surface defect detection methods based on machine vision were reviewed in this paper. First of all,the various imaging techniques and common defect types of solar cells surface were summarized. Secondly,the existing detection methods were introduced and compared with each other according to the different idea of mathematical modeling. Finally,a brief summary of this article and perspective of future research are presented. It can be concluded that the solar cell surface defect detection methods based on machine vision have made great progress. However,there is still room for improvement in algorithm design of feature extraction,such as feature extraction algorithm based on deep neural networks.

关 键 词：太阳能电池片 机器视觉 表面缺陷 成像 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]