Starnet-tex: lightweight real-time text detection via middle-order enhanced contextual attention

In recent years, scene text detection has garnered significant academic attention due to its broad applications. Existing methods still struggle to efficiently achieve precise localization of arbitrary-shaped text in complex natural scenes, particularly facing notable challenges in balancing detection accuracy and inference speed. To address this issue, this paper proposes StarNet-Tex, a lightweight real-time scene text detection framework. First, we propose a Contextual Attention Tail module (CAT) to address the long-range dependency deficiencies in shallow backbone networks. It achieves this through synergistic multi-directional large-kernel receptive field expansion and local feature enhancement, ultimately constructing the lightweight backbone network StarCAN. Second, we design a Channel Aggregation Feature Pyramid (CAFP) with cascaded U-shaped structures, which enhances middle-order feature representation through dynamic balancing of low- and high-order interactions. Furthermore, we introduce a Spatial-Channel Reconstruction Module (SCRM) to suppress background interference in dense text scenarios. This module employs cross-reconstruction strategies and channel-spatial decoupling mechanisms, thereby effectively reducing false detection rates. Experimental results demonstrate that StarNet-Tex achieves F-measure scores of 85.1%, 86.5%, and 85.8% on the CTW1500, Total-Text, and ICDAR2015 datasets, respectively. With only 3.3M parameters and real-time inference at 50.9 FPS, our framework provides a novel solution that synergistically optimizes accuracy and efficiency for text detection in resource-constrained scenarios. The code will be available at https://github.com/radish512/StarNet-Tex .