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In traffic safety, especially with the development of autonomous vehicles, TSR systems can play a critical role. However, their performances are considerably affected in poor environments where visibility and quality are degraded. This survey paper will comprehensively review the methods developed to address these challenges and outline various limitations of traditional TSR methods during such conditions. This aims to analyze the approaches using deep learning, image enhancement, and sensor fusion that enhance recognition accuracy in adverse weather and lighting conditions. Further, the applicability of these methods in practical situations is discussed, and gaps in current research are identified together with possible research directions for study in enhancing robustness and reliability in TSR systems operating under challenging environmental conditions.

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Traffic Sign Recognition in Adverse Environments: A Survey on Methods for Low Light, Fog, and Rain Conditions

  • 会议论文

  • 发表日期：

    2025年

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  • Kaushal Patel,
  • Sheshang Degadwala

摘要

In traffic safety, especially with the development of autonomous vehicles, TSR systems can play a critical role. However, their performances are considerably affected in poor environments where visibility and quality are degraded. This survey paper will comprehensively review the methods developed to address these challenges and outline various limitations of traditional TSR methods during such conditions. This aims to analyze the approaches using deep learning, image enhancement, and sensor fusion that enhance recognition accuracy in adverse weather and lighting conditions. Further, the applicability of these methods in practical situations is discussed, and gaps in current research are identified together with possible research directions for study in enhancing robustness and reliability in TSR systems operating under challenging environmental conditions.