张杰

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 2 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。

patent

1. 基于多尺度标准化流的无监督笔记本外观缺陷检测方法 赵云波, 张杰, 李泽瑞, 康宇, and 吕文君 [Abs]

   本发明涉及工业缺陷检测技术领域，公开了 一种基于多尺度标准化流的无监督笔记本外观 缺陷检测方法，将采集得到的笔记本电脑外观图 像，依次输入到多尺度特征提取网络模型以及缺 陷检测模型，得到检测结果；训练方法包括：获取 笔记本电脑的原始外观图像后进行数据预处理， 得到训练数据集；构造基于ResNet50网络和特征 金字塔网络的多尺度特征提取网络模型，提取训 练数据集中外观图像的多尺度特征；构造基于多 尺度标准化流网络的缺陷检测模型，以多尺度特 征作为缺陷检测模型的输入，通过计算损失函数 对缺陷检测模型进行训练。本发明能很好地定位 不同尺度不同类型的缺陷，有着良好的检测效果 与缺陷定位效果。

2. 一种基于多尺度标准化流的笔记本外观缺陷检测方法 赵云波, 张杰, 李泽瑞, 康宇, and 吕文君

Conference Articles

1. Defect Detection of Laptop Appearance Based on Improved Multi-Scale Normalizing Flows Jie Zhang, Yunbo Zhao, and Zerui Li In 38th Youth Acad. Annu. Conf. Chin. Assoc. Autom. 2023 [Abs] [pdf]

   In the laptop production process, timely detection of appearance defects is essential to ensure product quality. At present, there are many shortcomings in the manual visual inspection-based method on the laptops production line. In addition, due to the wide variety of laptop appearance defects and extreme differences in defect scales, existing defect detection algorithms perform poorly in the field of laptop appearance inspection. In response to the above problems, this paper proposes a defect detection algorithm based on improved multi-scale normalizing flows. First, the multi-level features extracted from the backbone network are fused by using the pyramid feature fusion module to obtain multi-scale features with rich semantic and spatial information. Then, the effective density estimation of the multi-scale features is achieved by fusing the normalizing flows of attention mechanisms. Finally, the defects are detected and localized based on the output likelihood values. The experimental results demonstrate the effectiveness of the proposed method in detecting and locating appearance defects.