Scene text detection has been widely studied on haze‐free images with reliable ground truth annotation. However, detecting scene text in inclement weather conditions remains a major challenge due to the severe domain distribution mismatch problem. This paper introduces a domain adaptation curriculum learning method to address this problem. The scene text detector is self‐trained in an easy‐to‐hard manner using the pseudo‐labels predicted from foggy images. Thus, our method reduces the pseudo‐labeling noise level. Then, a feature alignment module is introduced to help the network learn domain‐invariant features by training a domain classifier. Experimental results show that our method improved significantly on both synthetic foggy data sets and natural foggy data sets, outperforming many state‐of‐the‐art scene text detectors. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

中文翻译：

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恶劣天气条件下场景文本检测的领域适应课程学习

场景文本检测已在具有可靠地面实况注释的无雾图像上进行了广泛研究。然而，由于严重的域分布不匹配问题，在恶劣天气条件下检测场景文本仍然是一个重大挑战。本文介绍了一种领域适应课程学习方法来解决这一问题。场景文本检测器使用从有雾图像预测的伪标签以由易到难的方式进行自我训练。因此，我们的方法降低了伪标签噪声水平。然后，引入特征对齐模块，通过训练域分类器来帮助网络学习域不变特征。实验结果表明，我们的方法在合成雾数据集和自然雾数据集上都有显着改进，优于许多最先进的场景文本检测器。 © 2024 日本电气工程师协会和 Wiley periodicals LLC。