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<p>Traffic signs are crucial for keeping our roads safe and efficient. The number of traffic signs in existing datasets is restricted type and difficult circumstances. Because of the lack of metadata relating to these conditions, it is impossible to analyse the effect of a single element. Several climatic conditions are changing at the same time. In order to overcome the existing dataset’s flaws, we take a different approach. An improved convolutional neural network (CNN) technique to address the entire recognition process is automated. Offer a number of enhancements that are tested on traffic sign detection and lead to improved performance. This method is recognised 1000 traffic-sign recognition categories are included in real time image dataset. We present a detailed investigation of machine learning strategy for detecting signs with considerable intra-category variation, demonstrating error rates of less than 3% the proposed method. We needed to look at the average performance of a traffic sign recognition system employing upgraded CNN architecture, and we discovered that detection performance can drop dramatically under difficult conditions. Enhance precision and accuracy in difficult weather conditions such as snow, haze, rain, darkness, noise, and blur. Our different domain analysis revealed the simulated difficult conditions of detector performance.</p>

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Enhanced R-CNN Model for Traffic Sign Recognition under Diverse Environmental Conditions

  • 期刊论文

  • 发表日期：

    2025年3月3日

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  • Vijay Rajendran,
  • Uma Maheswari Subramaniyan,
  • Kannadhasan Suriyan

摘要

Traffic signs are crucial for keeping our roads safe and efficient. The number of traffic signs in existing datasets is restricted type and difficult circumstances. Because of the lack of metadata relating to these conditions, it is impossible to analyse the effect of a single element. Several climatic conditions are changing at the same time. In order to overcome the existing dataset’s flaws, we take a different approach. An improved convolutional neural network (CNN) technique to address the entire recognition process is automated. Offer a number of enhancements that are tested on traffic sign detection and lead to improved performance. This method is recognised 1000 traffic-sign recognition categories are included in real time image dataset. We present a detailed investigation of machine learning strategy for detecting signs with considerable intra-category variation, demonstrating error rates of less than 3% the proposed method. We needed to look at the average performance of a traffic sign recognition system employing upgraded CNN architecture, and we discovered that detection performance can drop dramatically under difficult conditions. Enhance precision and accuracy in difficult weather conditions such as snow, haze, rain, darkness, noise, and blur. Our different domain analysis revealed the simulated difficult conditions of detector performance.