MPGI‐Terminal defect detection based on M‐FRCNN

Abstract

Micro‐precision glass insulated terminal (MPGI‐Terminal) is a high‐precision and widely used micro‐precision component. The MPGI‐Terminal is small in size with 2–5 mm in diameter. It is difficult to distinguish missing blocks on the surface during manual inspection, which is prone to false detection and missing detection. The advantages of deep learning methods in the field of object detection have gradually highlighted. They can be used to construct the missing block detection model of MPGI‐Terminal. A Max‐Min Intersection over Union (MIoU) loss function based on overlap area, distance and aspect ratio is proposed, which helps the model to perform bounding box regression more reasonably, so as to obtain the object position more accurately. In addition, the Spatial Attention Module (SAM) and the Region of Interest Alignment (RoI Align) module are introduced to further improve the model performance. Based on the above modules, a MPGI‐Terminal missing block detection model based on M‐FRCNN is constructed. The results show that the proposed model can meet the requirements of fast and high accuracy of MPGI‐Terminal missing block detection.