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Received：04 March 2026，
Revised：2026-04-29，
Accepted：07 May 2026，
Online First：07 May 2026，
移动端阅览
目的
2
现有基于分割的场景文本检测方法多默认不同尺度特征可在同一语义空间中直接融合，采用统一监督信号驱动多尺度特征学习，忽略了跨层特征在语义层级上的本质差异，易导致低层像素噪声与高层语义约束相互干扰，从而影响检测性能。提出了一种基于尺度内分布感知与跨语义协同推理的高效场景文本检测方法。
方法
2
将像素级文本标注提升为多层次分布感知监督，引导不同尺度特征分支自主学习其对应感受野下的文本分布语义；在此基础上，引入跨语义全局知识集成机制，对多层次特征进行尺度内增强与跨层次协同融合，从而提升模型对复杂文本结构的整体建模能力。所引入的分支自主分布感知建模仅在训练阶段启用，测试阶段无需额外计算，保证了检测精度与推理效率之间的良好平衡。
结果
2
在多个公开数据集上，与现有10余种先进方法进行对比，本文方法均取得显著提升。相较于先进方法DBNet++（differentiable binarization network++），提出方法的F值在Total-Text、MSRA-TD500（MSRA text detection 500 database）、CTW（Curve Text in the Wild）1500数据集上分别提升了4.2%、5.0%和2.6%。消融实验进一步验证了所提出模块的有效性。
结论
2
实验结果表明，提出方法在多种场景下均具备良好的检测性能，同时保持较高的推理效率，验证了提出方法在高效场景文本检测任务中的可行性。
Objective
2
Scene text detection （STD） is a fundamental task in scene text reading and understanding， and plays an important role in enabling intelligent systems to perceive high-level semantic information from natural scenes. It provides essential technical support for various applications， such as autonomous driving， image retrieval， unmanned systems， and intelligent scene analysis. In recent years， with the rapid development of deep learning and visual representation modeling， STD has achieved substantial progress and attracted increasing research attention. Existing deep learning-based methods can generally be divided into regression-based， connected-component-based， and segmentation-based approaches. Among them， segmentation-based methods have become a mainstream solution due to their flexibility in pixel-level prediction and strong capability in detecting arbitrarily shaped text instances. However， most existing segmentation-based methods still implicitly assume that multi-scale features can be optimized under a unified supervision signal and fused within a shared semantic space. Such a strategy overlooks the intrinsic semantic heterogeneity across feature hierarchies. Specifically， low-level features contain rich spatial details but are vulnerable to pixel-level noise， whereas high-level features encode stronger semantic information but may lose fine-grained structural cues. Directly supervising and fusing these heterogeneous representations may lead to interference between low-level pixel noise and high-level semantic constraints， thereby weakening feature fusion effectiveness and reducing inference stability. From the perspective of representation learning， multi-scale features are not merely homogeneous representations at different spatial resolutions， but heterogeneous representations associated with different semantic granularities. Therefore， effective STD requires explicit modeling， alignment， and coordination of semantic information across different feature levels.
Method
2
To address the above issues， we propose an efficient and effective STD framework， which consists mainly of a branch-wise distribution-aware modeling （BDM） module and a cross-semantic global knowledge integration （CGKI） module. Considering that conventional multi-scale text detection methods often ignore the semantic discrepancies among different feature levels at the supervision stage， the BDM module is designed from the perspective of label modeling. Specifically， it transforms pixel-level binary segmentation annotations into hierarchical distribution-aware supervision signals， enabling feature branches at different scales to independently learn text distribution semantics that are consistent with their corresponding receptive fields. In this way， the semantic interference among heterogeneous multi-scale features can be alleviated， and semantically aligned feature representations can be provided for subsequent feature fusion. Notably， the BDM module is only employed during the training stage and removed during inference， thus improving detection accuracy without introducing additional computational overhead. On the basis of intra-scale distribution-aware semantic alignment， we further design the CGKI module to explicitly model the collaborative relationships among different semantic levels. This module first enhances the representation of each scale within its own semantic space， and then performs controlled cross-scale interaction through adaptive scale reweighting and adjacent-scale information injection. By selectively recalibrating the importance of different scales and introducing complementary contextual information from neighboring levels， the CGKI module achieves global coordination and stable fusion of multi-scale semantics while maintaining a controllable computational cost. The ResNet equipped with deformable convolutions and feature pyramid network （FPN） is adopted as the backbone. For the training stage， the model is either directly trained on public datasets for ablation studies or pre-trained on Synth150k for 10 epochs and then fine-tuned on real-world datasets for comparison experiments. SGD with an initial learning rate of 0.001 and a poly learning rate schedule is used for optimization， together with data augmentation strategies including random rotation， cropping， and flipping.
Result
2
The proposed method is extensively evaluated against more than ten advanced methods on five widely used public text detection benchmarks， including MSRA-TD500， CTW1500， Total-Text， ICDAR2015， and MPSC. Precision （P）， recall （R）， and F-measure （F） are adopted as the evaluation metrics， where higher values indicate better detection performance. All inference tests are conducted on a single NVIDIA GTX 1080Ti GPU with an Intel i7-6800K CPU to ensure a consistent evaluation environment. Experimental results show that the proposed method consistently outperforms existing efficient STD methods on the above datasets while maintaining competitive inference speed. Specifically， on Total-Text， the proposed method improves the F-measure by 4.2% and 2.7% compared with DBNet++ and FEPE， respectively. On MSRA-TD500， it achieves F-measure improvements of 5.0% and 4.1% over DBNet++ and FEPE， respectively. On CTW1500， it gains 2.6% and 1.0% in F-measure against DBNet++ and FEPE， respectively. On ICDAR2015， it achieves F-measure gains of 2.8% and 2.7% relative to DBNet++ and FEPE， respectively. On the industrial scene text dataset MPSC， the proposed method surpasses existing advanced methods ISTD-DLA， ODM， and RT-DETR by 1.0%， 3.8%， and 1.3% in F-measure， respectively. Ablation studies on MSRA-TD500 further demonstrate the effectiveness of the proposed modules， confirming that BDM and CGKI can enhance multi-scale feature representation and fusion. In addition， visualization results on these datasets show that the proposed method can generate complete and accurate text boundaries in different scenes. Cross-dataset experiments further verify the generalization ability of the proposed method， where it achieves superior performance over existing representative methods such as ZTD， MTD， and CM-Net under both line-level and word-level annotation settings.
Conclusion
2
This work presents an efficient and effective scene text detection method. By integrating BDM and CGKI， the proposed method enhances the semantic consistency and collaborative fusion of multi-scale text features， thereby improving the detection of complex text. Experimental results on multiple public benchmarks demonstrate that the proposed method achieves competitive detection accuracy and inference speed， outperforming existing efficient scene text detection methods. In future work， we will explore the integration of the proposed detection model with efficient text recognition models to establish an end-to-end efficient framework for text spotting.
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