桂旺友

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 1 篇，投出待录用论文0篇。

patent

1. 用于PCB微小缺陷检测的单帧目标检测方法及存储介质 许镇义, 桂旺友, 曹洋, 康宇, and 赵云波 [Abs]

   本发明的一种用于PCB微小缺陷检测的单帧目标检测方法及存储介质,包括以下步骤：S1、获取PCB图像信息,并对图像进行数据预处理；S2、构建网络模型,将处理后的图像输入VGG-16特征提取网络,并对不同层次的特征进行融合,同时消除融合过程中所带来的负面影响；S3、对模型进行训练,并根据训练得到的结果评估性能。本发明利用注意机制来学习跨通道融合的特征之间的关系,并利用shuffle模块消除融合后的混叠效应。提出了非最大抑制方法,以减轻PCB图像的重叠效应。语义上升模块通过将不同层次的特征进行融合,不仅使低层次的特征具备丰富的语义信息,还能让高层次的特征具备更好的回归性,在目标分类与定位方面显著增强,能够更好地适应微小目标的检测。

Conference Articles

1. A Real-time Detection Method for SMT Chip Component Defects Based on Adaptive Collaborative Feature Yunbo Zhao, Wangyou Gui, Yu Kang, Kehao Shi, Lijun Zhao, and Zhenyi Xu In 2024 International Conference on Guidance, Navigation and Control (ICGNC 2024) 2024 [Abs] [pdf]

   Surface Mounted Technology (SMT) is an electronic assembly process that involves the placement of electronic components on a printed circuit board. In the SMT process, defect detection technology is the key to controlling the quality of electronic products. In the Industry, AOI technology based on image processing is widespread. However, it is plagued by several challenges including slow response time and a high defect mis-detection rate, warranting the need for further research and advancement. In recent years, researchers have turned to deep learning-based target detection algorithms for industrial defect detection. Nevertheless, in scenarios such as SMT, complexities arising from intricate object shapes and the challenge of balancing accuracy and speed present significant hurdles, rendering common target detection algorithms inadequate for meeting the demands of these scenarios. To solve these problems, this paper proposes a real-time detection method for SMT chip component defects based on adaptive collaborative feature (SMT-DETR), which employs an adaptive collaborative feature extraction module ACBlock to the deformate features of the object, and can pay attention to the defective changes of the chip components effectively. Secondly, a new IMIoU loss is proposed in this paper, which can capture the tiny object information more effectively and has faster convergence speed by combining the efficient IoU loss function. Finally, experiments show that the proposed method is better compared to classical object detection algorithms.