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    YOLOv10-CBRC: A high-precision document image layout analysis model by Zhenjie Wu, Weilan Wang, Hongrui Li

    Published 2025-07-01
    “…Building upon YOLOv10 as the baseline model, the proposed model implements three key improvements: (1) the replacement of the Partial Self-Attention (PSA) module with the Convolutional Block Attention Module (CBAM), which enhances perception of channel-wise information and spatial localization of objects,(2) the adoption of dual-branch Re-upsample and Re-SCDown modules (2Re), which facilitates more effective utilization of multi-scale information,(3) the design of a novel classification feature processor, CIBwithResidualCV3 (CRCV3), which improves performance in classification tasks.Experimental results demonstrate that YOLOv10-CBRC achieves a mAP $$_{50\text {-}95}$$ 50 - 95 of 77.6% on the AbaTND, while YOLOv10-RC reaches mAP $$_{50\text {-}95}$$ 50 - 95 scores of 70.6% and 74.6% on the $$D^4LA$$ D 4 L A and IIIT-AR-13K datasets, respectively, significantly outperforming baseline model. …”
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