NIC Lab | 余程凯

个人信息

共撰写/参与撰写专利 1 项，录用/发表论文 1 篇，投出待录用论文0篇。联培学生可能有其他不在此展示的论文/专利。

印刷电路板的微小缺陷检测方法及存储介质许镇义, 余程凯, 曹洋, 康宇, and 赵云波 [Abs]
本发明的一种印刷电路板的微小缺陷检测方法及存储介质,包括以下步骤,S1、获取PCB缺陷样本数据并进行数据预处理；S2、在PCB训练集的边界框上使用k-means聚类来找到符合要求的anchor尺度；S3、采用多尺度特征金字塔结构提取特征,对主干卷积网络中得到自底向上的特征图进行上采样,得到自顶向下的特征图,然后将其与自底向上的特征图逐元素相加得到最终的特征图；S4、通过计算损失,训练网络参数。本发明通过数据增强技术提供深度学习所需要的充足训练数据,利用k-means聚类设计合理的锚点尺度,再将特征金字塔与Faster R-CNN网络相结合,加强了不同层次特征图之间的关系,从而实现对PCB微小缺陷的检测。本发明提高检测效率,并且能够适应多种缺陷检测,适应性强。

SMT Component Defection Reassessment Based on Siamese Network Chengkai Yu, Yun-Bo Zhao , and Zhenyi Xu In Methods and Applications for Modeling and Simulation of Complex Systems 2022 [Abs] [doi] [pdf]
In the SMT process, after component placement, checking the quality of component placement on the PCB board is a basic requirement for quality control of the motherboard. In this paper, we propose a deep learning-based classification method to identify the quality of component placement. This is a comparison method and the novelty is that the siamese network is trained to extract the features of the standard placement component map and the placement component map to be inspected and output the probability of similarity between the two to determine the goodness of the image to be inspected. Compared to traditional hand-crafted features, features extracted using convolutional neural networks are more abstract and robust. In addition, during training, the concatenated network pairs the sample images to expand the amount of training data, increasing the robustness of the network and reducing the risk of overfitting. The experimental results show that this method has better results than the general model for the classification of placement component images.