摘要
现有基于机器视觉的太阳能电池片表面缺陷检测算法均是采用各种类型的数学模型来进行算法设计,为进一步提高检测准确率,从人眼仿生学角度出发,首次将人眼的视觉注意机制引入到太阳能电池片表面缺陷检测中,提出了一种基于视觉显著性的太阳能电池片表面缺陷检测算法。首先,对输入的太阳能电池片表面图像进行预处理,去除对检测有影响的噪声和栅线;其次,提出一种基于自学习特征的视觉显著性检测算法来大致定位缺陷区域;随后,提出一种视觉显著性和超像素分割相结合的算法来进一步精确定位缺陷区域;最后,通过形态学后处理得到最终检测结果。在包含多种缺陷类型的测试图像库上的主观和客观实验评估表明,该算法具有较高的检测准确率。
The existing solar cell surface defect detection algorithms based on machine vision are all designed to use various types of mathematical models to carry out the algorithm design. In order to further improve the detection accuracy, inspired by human vision bionics, the human visual attention mechanism is firstly introduced in the solar cell surface defect detection, and a solar cell surface defect detection algorithm based on visual saliency is proposed in this paper. First of all, the acquired solar cell surface image is preprocessed to remove the noise and grids that influence the defect detection. Secondly, a visual saliency detection algorithm based on self-learning features is put forward to roughly locate the defect region. Then, an algorithm that combines the visual saliency and superpixel segmentation is proposed to precisely locate the defect region. At last, the final detection result is obtained using morphological post-processing. The subjective and objective experiment evaluations on a test image database containing various types of defects demonstrate that the proposed algorithm has high detection accuracy.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2017年第7期1570-1578,共9页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(61501407
61503173)
河南省高等学校重点科研项目(15A413006)
河南省重大科技专项(161100211600)
河南省科技厅科技攻关项目(162102210060
172102210062)
郑州轻工业学院博士基金(2014BSJJ016
2016BSJJ006
2016BSJJ002)
郑州轻工业学院研究生科技创新基金(2016026)项目资助
