Localizing text in low-light environments is challenging due to visual degradations. Although a straightforward solution involves a two-stage pipeline with low-light image enhancement (LLE) as the initial step followed by detector, LLE is primarily designed for human vision instead of machine and can accumulate errors. In this work, we propose an efficient and effective single-stage approach for localizing text in dark that circumvents the need for LLE. We introduce a constrained learning module as an auxiliary mechanism during the training stage of the text detector. This module is designed to guide the text detector in preserving textual spatial features amidst feature map resizing, thus minimizing the loss of spatial information in texts under low-light visual degradations. Specifically, we incorporate spatial reconstruction and spatial semantic constraints within this module to ensure the text detector acquires essential positional and contextual range knowledge. Our approach enhances the original text detector's ability to identify text's local topological features using a dynamic snake feature pyramid network and adopts a bottom-up contour shaping strategy with a novel rectangular accumulation technique for accurate delineation of streamlined text features. In addition, we present a comprehensive low-light dataset for arbitrary-shaped text, encompassing diverse scenes and languages. Notably, our method achieves state-of-the-art results on this low-light dataset and exhibits comparable performance on standard normal light datasets. The code and dataset will be released.

翻译：在低光照环境下定位文本具有挑战性，主要归因于视觉退化。尽管一种直接的解决方案采用两阶段流程，即以低光照图像增强作为初始步骤，随后使用检测器，但低光照图像增强主要针对人类视觉而非机器设计，且可能累积误差。本研究提出了一种高效且有效的单阶段方法，用于在暗环境中定位文本，从而避免了对低光照图像增强的需求。我们在文本检测器的训练阶段引入了一个约束学习模块作为辅助机制。该模块旨在引导文本检测器在特征图缩放过程中保留文本空间特征，从而最小化低光照视觉退化下文本空间信息的损失。具体而言，我们在该模块中整合了空间重建与空间语义约束，确保文本检测器获取必要的位置和上下文范围知识。我们的方法通过动态蛇形特征金字塔网络增强了原始文本检测器识别文本局部拓扑特征的能力，并采用自底向上的轮廓塑造策略，结合新颖的矩形累积技术，以精确描绘流线型文本特征。此外，我们提出了一个涵盖多样化场景与语言的综合性低光照任意形状文本数据集。值得注意的是，我们的方法在该低光照数据集上取得了最优结果，并在标准正常光照数据集上表现出相当的性能。代码和数据集将公开提供。