多特征融合的太阳能电池片缺陷检测  

Defect detection of solar cells based on multi-feature fusion

作　　者：赖天舒 刘怀广[1,2,3] 汤勃 周诗洋 LAI Tianshu;LIU Huaiguang;TANG Bo;ZHOU Shiyang(Key Laboratory of Metallurgical Equipment and Control Technology(Ministry of Education),Wuhan University of Science and Technology,Wuhan 430081,China;Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering,Wuhan University of Science and Technology,Wuhan 430081,China;Precision Manufacturing Institute,Wuhan University of Science and Technology,Wuhan 430081,China)

机构地区：[1]武汉科技大学冶金装备及其控制教育部重点实验室,湖北武汉430081 [2]武汉科技大学机械传动与制造工程湖北省重点实验室,湖北武汉430081 [3]武汉科技大学精密制造研究院,湖北武汉430081

出　　处：《应用光学》2023年第3期605-613,共9页Journal of Applied Optics

基　　金：国家自然科学基金(51874217);国家自然科学基金青年项目(51805386)。

摘　　要：太阳能电池片在生产过程中,因工序或材料原因会导致其存在缺陷。基于光致发光成像原理,提出了一种基于背景评估的太阳能电池片图像增强方法,以及一种基于形态特征和HOG特征融合的缺陷识别方法。首先分析了电池片缺陷的形态和位置特点,提出了缺陷两步分割法,对分割的缺陷提取多方向HOG特征,采取拉普拉斯特征映射法对HOG特征进行降维;然后融合长宽比、圆形度等形态特征;最后针对支持向量机(support vector machines,SVM)中的核函数和惩罚因子,采用粒子群算法(particle swarm optimization,PSO)加以优化,改善了缺陷分类效果。应用所确立的算法对50幅图像进行检测,分类识别的准确率最高可达98.3%。将新算法与传统的SVM算法以及Le-Net网络等进行对比,可知新算法具有较高的识别准确率。The existence of defects in solar cells due to the process or material reasons in the production process.Based on the photoluminescence imaging principle,an image enhancement method for solar cells based on background assessment and a defect recognition method based on morphological feature and HOG feature fusion were proposed.Firstly,the characteristics of shape and location of cell defects were analyzed,and the two-step segmentation method was proposed to extract multi-directional HOG features from the segmented defects,and Laplace feature mapping method was adopted to reduce the dimension of HOG features.Then,the morphological characteristics such as aspect ratio and circularity were fused.Finally,according to the kernel function and penalty factor in support vector machines(SVM),the particle swarm optimization(PSO)algorithm was optimized to improve the defect classification effect.Fifty images were detected by using the proposed method,and the accuracy of classification recognition reached 98.3%.Comparing the proposed algorithm with the traditional SVM algorithm and Le-Net network,it can be seen that the proposed algorithm has the higher recognition accuracy.

关 键 词：太阳能电池片 支持向量机 缺陷检测 特征压缩 粒子群算法 

分 类 号：TN29[电子电信—物理电子学]