Han Xixi, Li Renpeng, Wang Boqin, Lin Zhibo
School of Electronic Information, Zhongyuan University of Technology, Zhengzhou, Henan, China.
Anyang Iron and Steel Automation Software Co., Ltd, Zhengzhou, Henan, China.
PeerJ Comput Sci. 2024 Feb 28;10:e1900. doi: 10.7717/peerj-cs.1900. eCollection 2024.
The aim of this article is to propose a defect identification method for bare printed circuit boards (PCB) based on multi-feature fusion. This article establishes a description method for various features of grayscale, texture, and deep semantics of bare PCB images. First, the multi-scale directional projection feature, the multi-scale grey scale co-occurrence matrix feature, and the multi-scale gradient directional information entropy feature of PCB were extracted to build the shallow features of defect images. Then, based on migration learning, the feature extraction network of the pre-trained Visual Geometry Group16 (VGG-16) convolutional neural network model was used to extract the deep semantic feature of the bare PCB images. A multi-feature fusion method based on principal component analysis and Bayesian theory was established. The shallow image feature was then fused with the deep semantic feature, which improved the ability of feature vectors to characterize defects. Finally, the feature vectors were input as feature sequences to support vector machines for training, which completed the classification and recognition of bare PCB defects. Experimental results show that the algorithm integrating deep features and multi-scale shallow features had a high recognition rate for bare PCB defects, with an accuracy rate of over 99%.
本文旨在提出一种基于多特征融合的裸印刷电路板（PCB）缺陷识别方法。本文建立了裸PCB图像的灰度、纹理和深度语义等各种特征的描述方法。首先，提取PCB的多尺度方向投影特征、多尺度灰度共生矩阵特征和多尺度梯度方向信息熵特征，以构建缺陷图像的浅层特征。然后，基于迁移学习，利用预训练的视觉几何组16（VGG-16）卷积神经网络模型的特征提取网络来提取裸PCB图像的深度语义特征。建立了一种基于主成分分析和贝叶斯理论的多特征融合方法。接着将浅层图像特征与深度语义特征进行融合，提高了特征向量表征缺陷的能力。最后，将特征向量作为特征序列输入支持向量机进行训练，完成裸PCB缺陷的分类与识别。实验结果表明，融合深度特征和多尺度浅层特征的算法对裸PCB缺陷具有较高的识别率，准确率超过99%。
