CFP-SAEF: A Spatial-Attention Enhanced Feature Fusion Network for Carbon Fiber Prepreg Defect Detection

Carbon fiber reinforced polymer (CFRP) composites are widely used in aerospace, automotive manufacturing, and new energy equipment due to their high specific strength, lightweight, and corrosion resistance. However, during the manufacturing process of carbon fiber prepreg, small-sized, morphologically diverse, and multi-scale surface defects—such as cracks, voids, wrinkles, and foreign matter—frequently occur. If not detected in time, these defects can severely compromise the structural strength and service life of CFRP components. To address this issue, this paper proposes a carbon fiber prepreg defect detection network based on spatial-attention enhanced feature fusion (CFP-SAEF). The proposed method employs EfficientNet and UniRepLKNet as complementary backbones to form a dual-encoder structure, integrates their multi-scale features through a Multi-stage Adaptive Feature Fusion Module (MAFFM), and incorporates a Spatial Attention Enhanced Feature Pyramid Network (SAE-FPN) to selectively strengthen responses to small defect regions. Experimental results demonstrate that, on the carbon fiber prepreg defect dataset, CFP-SAEF achieves an mAP of 93.82%, outperforming YOLOv8m by 3.61 percentage points, with AP gains of 4.1% and 5.6% for the slit and foreign matter categories, respectively. Furthermore, on the PASCAL VOC2007 and KolektorSDD datasets, CFP-SAEF also attains higher detection accuracy compared with existing methods, validating the effectiveness and generalization capability of the proposed approach.