史珂豪

个人信息

参与实验室科研项目

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。 联培学生可能有其他不在此展示的论文/专利。

patent

1. 贴片元件缺陷检测模型的训练方法、检测方法及装置 赵云波, 桂旺友, 许镇义, 史珂豪, and 王子曜 2024 [Abs] [pdf]

   本公开提供了一种贴片元件缺陷检测模型的训练方法、检测方法及装置，可以应用于电子产品质量检测领域。该方法包括：获取训练样本，训练样本包括与贴片元件样本相关的图像数据、缺陷位置标签和分类标签；根据级联的多个特征提取层对图像数据进行多级特征提取，得到末端级图像特征和中间级图像特征；将末端级图像特征输入至编码器，得到深层特征；将中间级图像特征和深层特征输入跨尺度特征融合网络，得到目标特征；将目标特征输入至解码器，得到缺陷分类预测结果和边界框预测结果；根据分类预测结果、边界框预测结果、缺陷位置标签和分类标签，训练贴片元件缺陷检测模型，得到训练后的贴片元件缺陷检测模型。

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.