余程凯

个人信息

参与实验室科研项目

人机智能协同关键技术及其在智能制造中的应用

非可信智能驱动的可靠智造

学术成果

共撰写/参与撰写专利 1 项，录用/发表论文 2 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。

patent

1. 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。

Journal Articles

1. TGSYOLO: Template-Guidance Siamese Network for SMT Welding Defect Detection Kehao Shi, Chengkai Yu, Yang Cao, Yu Kang, Yunbo Zhao, Lijun Zhao, and Zhenyi Xu IEEE Transactions on Components, Packaging and Manufacturing Technology 2024 [Abs] [doi] [pdf]

   Surface-mounted technology (SMT) welding defect detection plays a key role in the printed circuit board assembly (PCBA) production process, which affects the use of electronic products and cost. Previous works tend to realize defect detection with only defect samples and they assume that there are sufficient defect samples. However, defect samples are usually difficult to collect in real-life scenarios while enough template samples can be easily obtained. In addition, most existing works carry out defect detection based on benchmarks with simple backgrounds of PCBA, which is not suitable for PCBA with complex structures in modern electronic product manufacturing. To address the above issues, we propose a template-guidance Siamese network based on YOLO for SMT welding defect detection (TGSYOLO), which is deployed on a real SMT automatic optical inspection (AOI) system. First, the two-stream structure is introduced to extract deep features in defect images and template images, in which template features serve as guidance knowledge. Then, a template fusion Transformer (TFT) is proposed to model global features between detect and template features in the low-level stage, which could acquire long-range correlations to force the network to focus on potential defect regions. Next, to avoid the disappearance of tiny defect features during deep feature fusion, a multiscale attention feature pyramid network (MAFPN) is proposed to directly fuse defect semantic information from low-level features, which retains detailed expressions of defects through skip connection and obtains compact fusion features. Furthermore, we collect limited welding defect samples based on more complex PCBA backgrounds than previous works through a real SMT AOI system. Experiments on the limited dataset show that TGSYOLO could reach 0.985 of mAP@0.5, 0.885 of mAP@0.75, and 0.984 of F1, which is 0.008, 0.054, and 0.025 higher than other SOTA methods. Also, generalization experiments on the public DeepPCB show that TGSYOLO could still reach the best with 0.991 of mAP@0.5 and 0.89 of mAP@0.75, which proves that TGSYOLO has good generalization performance.

Book Chapters

1. 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.

学位论文

毕业去向

杭州市临平区人民法院， 司法行政

外部链接

个人博客