A Novel Surface Defect Detection Method for Carbon Fiber Composite Plates Based on Super-Resolution Reconstruction and U-Net Network

Rapid and accurate detection of surface defects has become critical with the increasing demand for highly reliable carbon‑fiber composite plates (CFCPs) in advanced manufacturing. This study proposes a dual-stage enhancement framework to detect subtle defects in CFCPs, addressing the limitations of conventional down‑sampling and feature‑extraction methods, especially when samples are limited and defects are subtle. The framework highlights the critical role of expert knowledge in defect detection and allows effective parameter transfer between a task-specific super-resolution reconstruction module and a residual, multiscale fusion semantic segmentation network. Experiments on a digital‑radiography CFCPs data set demonstrate that this method markedly amplifies weak defect signatures and pinpoints their locations with high fidelity. The findings exhibit significant gains in precision, recall, F1‑score, and mean intersection‑over‑Union relative to U‑Net, SegNet, and other baselines. In small‑sample conditions, the proposed model nearly doubles the performance of the canonical U‑Net. This framework offers a broadly applicable solution for automated micro‑scale defect inspection across composite‑material systems and other advanced‑manufacturing contexts.