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<p>Text detection in natural scenes, particularly under challenging weather conditions like fog, remains a formidable task due to complex backgrounds, irregular text arrangements, and uneven illumination. This paper introduces a robust text detection method tailored for foggy traffic images by leveraging an improved Connectionist Text Proposal Network (CTPN) model. To enhance text clarity in foggy conditions, we incorporate a defogging pre-processing step inspired by atmospheric scattering models. Additionally, a hybrid post-processing module combining NMS and Soft-NMS is proposed to refine text detection results, particularly for horizontal texts. Experimental evaluations on the ICDAR2013 and HSText-1000 datasets demonstrate the superior performance of our method, achieving recall, precision, and F-score improvements of 16.13%, 2.96%, and 10.16%, respectively, over the original CTPN model on the HSText-1000 dataset. This work provides a promising architecture for text detection in adverse weather conditions.</p>

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Robust text detection in foggy traffic scenes using an enhanced CTPN model with de-fogging pre-processing

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月13日

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  • Chang Han,
  • Zhengqiang Xiong,
  • Yingying Liu,
  • Runmin Wang

摘要

Text detection in natural scenes, particularly under challenging weather conditions like fog, remains a formidable task due to complex backgrounds, irregular text arrangements, and uneven illumination. This paper introduces a robust text detection method tailored for foggy traffic images by leveraging an improved Connectionist Text Proposal Network (CTPN) model. To enhance text clarity in foggy conditions, we incorporate a defogging pre-processing step inspired by atmospheric scattering models. Additionally, a hybrid post-processing module combining NMS and Soft-NMS is proposed to refine text detection results, particularly for horizontal texts. Experimental evaluations on the ICDAR2013 and HSText-1000 datasets demonstrate the superior performance of our method, achieving recall, precision, and F-score improvements of 16.13%, 2.96%, and 10.16%, respectively, over the original CTPN model on the HSText-1000 dataset. This work provides a promising architecture for text detection in adverse weather conditions.