Pinhole Detection Results of Different Methods.

Metallized Ceramic Ring is a novel electronic apparatus widely applied in communication, new energy, aerospace and other fields. Due to its complicated technique, there would be inevitably various defects on its surface; among which, the tiny pinhole defects with complex texture are the most difficult to detect, and there is no reliable method of automatic detection. This Paper proposes a method of detecting micro-pinhole defects on surface of metallized ceramic ring combining Improved Detection Transformer (DETR) Network with morphological operations, utilizing two modules, namely, deep learning-based and morphology-based pinhole defect detection to detect the pinholes, and finally combining the detection results of such two modules, so as to obtain a more accurate result. In order to improve the detection performance of DETR Network in aforesaid module of deep learning, EfficientNet-B2 is used to improve ResNet-50 of standard DETR network, the parameter-free attention mechanism (SimAM) 3-D weight attention mechanism is used to improve Sequeeze-and-Excitation (SE) attention mechanism in EfficientNet-B2 network, and linear combination loss function of Smooth L1 and Complete Intersection over Union (CIoU) is used to improve regressive loss function of training network. The experiment indicates that the recall and the precision of the proposed method are 83.5% and 86.0% respectively, much better than current mainstream methods of micro defect detection, meeting requirements of detection at industrial site.金属化陶瓷环（Metallized Ceramic Ring）是一种新型电子器件，广泛应用于通信、新能源、航空航天等领域。由于其制备工艺复杂，表面不可避免地会出现各类缺陷；其中纹理复杂的微小针孔缺陷是最难检测的一类，且目前尚无可靠的自动检测方案。本文提出一种结合改进型检测DETR（Detection Transformer）网络与形态学运算的金属化陶瓷环表面微针孔缺陷检测方法，该方法通过基于深度学习与基于形态学的两个针孔缺陷检测模块分别完成缺陷检测，最终融合两个模块的检测结果以获得更精准的检测效果。为提升上述深度学习模块中DETR网络的检测性能，本研究采用EfficientNet-B2替换标准DETR网络中的ResNet-50骨干网络；针对EfficientNet-B2网络中的压缩与激励（Squeeze-and-Excitation，SE）注意力机制，使用无参数三维权重注意力机制（SimAM）进行替换；同时采用平滑L1（Smooth L1）与完全交并比（Complete Intersection over Union，CIoU）的线性组合损失函数，改进训练网络的回归损失函数。实验结果表明，所提方法的召回率与精确率分别达到83.5%与86.0%，显著优于当前主流的微缺陷检测方法，可满足工业现场的检测需求。