High-throughput defect detection and coverage quantification of graphene grids via a dual-stage deep learning framework

Abstract Graphene grids exhibit exceptional loading capacity for macromolecules, single atoms, and nanoparticles, offering significant potential for exploring the structure and properties of various materials at the nanoscale. However, challenges such as carbon film rupture, contamination, and uneven graphene film coverage frequently occur during grid fabrication. Here we propose a dual-stage deep learning model integrating U-Net and an enhanced YOLO11 architecture, enabling efficient and accurate defect detection and graphene coverage quantification. A tailored data augmentation strategy expanded the initial defect dataset by more than an order of magnitude, which directly contributed to an overall 11.72% improvement across the model’s performance metrics. With the integration of the multi-scale convolutional attention (MSCA) module and the slicing-aided hyper inference (SAHI) method, the model achieved a 0.67% mean absolute percentage error (MAPE), while reducing the average detection time from 26.6 to 0.1 min per image. The proposed model holds strong potential for extension to various material characterization image analysis tasks, providing a scalable strategy for high-throughput image processing that bridges fundamental research with industrial-scale applications.