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

Yixiong Feng; Peiyan Pan; Qi Kong; Bingtao Hu; Zhenghao Sun; Junliang Wang; Jianrong Tan

doi:10.1016/j.eng.2025.09.033

Engineering ›› :202509033 DOI: 10.1016/j.eng.2025.09.033

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

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Abstract

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.

Keywords

Carbon fiber composite plates / Defect detection / Super-resolution reconstruction / Residual learning / Semantic-segmentation / Digital radiography

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Yixiong Feng, Peiyan Pan, Qi Kong, Bingtao Hu, Zhenghao Sun, Junliang Wang, Jianrong Tan. A Novel Surface Defect Detection Method for Carbon Fiber Composite Plates Based on Super-Resolution Reconstruction and U-Net Network. Engineering 202509033 DOI:10.1016/j.eng.2025.09.033

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