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：在低光照环境下进行文本定位极具挑战性，主要源于视觉退化问题。虽然直观的解决方案采用两阶段流水线（先进行低光照图像增强，再接入检测器），但低光照图像增强技术本质为人类视觉而非机器视觉设计，且可能产生误差累积。本文提出一种高效的单阶段暗光文本定位方法，无需依赖低光照图像增强环节。我们引入约束学习模块作为文本检测器训练阶段的辅助机制，该模块旨在引导检测器在特征图缩放过程中保留文本空间特征，从而减少低光照视觉退化造成的文本空间信息损失。具体而言，该模块通过空间重建约束与空间语义约束，确保检测器获取关键的位置与上下文范围知识。为增强原始检测器对文本局部拓扑特征的识别能力，我们构建动态蛇形特征金字塔网络，并采用基于新型矩形累积技术的自底向上轮廓塑形策略，实现流线型文本特征的精准描绘。此外，本文发布了涵盖多场景多语种的任意形状文本低光照综合数据集。实验表明，该方法在低光照数据集上取得最优性能，同时在常规光照标准数据集上展现可比效果。相关代码与数据集将开源发布。