基于改进RT-DETR的极端天气下交通标志检测方法
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1.上海电力大学电子与信息工程学院 上海 201306;2.北京交通大学自动化与智能学院 北京 100044

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TP391.41;TN911

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国家自然科学基金面上项目(62073024)资助


Improved RT-DETR based method for traffic sign recognition in extreme weather
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1.College of Electronic and Information Engineering, Shanghai University of Electric Power,Shanghai 201306, China;2.School of Automation and Intelligence, Beijing Jiaotong University,Beijing 100044, China

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    摘要:

    针对雨、雾和雪等极端天气下交通标志模糊不清,导致检测精度下降和小目标识别困难等问题,本文提出了一种基于改进RT-DETR的交通标志检测算法。首先,采用数据增强方法模拟极端天气环境,以提高模型在这些环境下对交通标志的识别能力。其次,在主干网络中引入Ortho注意力机制,利用正交滤波器减少特征冗余,筛选重要通道信息,提高对小目标的检测精度。此外,采用高层筛选特征金字塔网络(HS-FPN)替换原模型中的跨尺度上下文特征混合器(CCFM),通过高层特征筛选并融合低层特征信息,提升模型在极端天气下对低对比度和模糊目标的检测精度。实验结果显示,改进算法在平均检测精度方面达到87.84%,相比原始RT-DETR模型提高了2.37%,同时参数量减少至18.22 M,相比原模型降低了8.4%,对小目标和处于极端天气中的目标识别精度更高,对保障乘客的安全具有实际意义。

    Abstract:

    To address the issues of decreased detection accuracy and difficulty in small object recognition caused by blurred traffic signs in extreme weather conditions such as rain, fog, and snow, this paper proposes a traffic sign detection algorithm based on an improved RT-DETR. First, data augmentation is applied to the TT100K dataset under simulated extreme weather conditions to enhance the model′s ability to recognize traffic signs in these environments. Second, the Ortho attention mechanism is introduced into the backbone network, which uses orthogonal filters to reduce feature redundancy and prioritize essential channel information, thereby improving the model′s detection accuracy for small objects. Additionally, a high-level screening-feature pyramid network (HS-FPN) replaces the cross-scale contextual feature mixer (CCFM) in the original model. HS-FPN filters and merges low-level feature information using high-level features, enhancing the model′s detection accuracy for low-contrast and blurred targets in extreme weather conditions. Experimental results show that the proposed improved algorithm achieves an average detection accuracy of 87.84%, an improvement of 2.37% over the original RT-DETR model, while reducing the parameter count to 18.22 M, an 8.4% reduction compared to the original model. The model demonstrates higher accuracy in recognizing small objects and targets under extreme weather, contributing significantly to passenger safety.

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秦伦明,张云起,崔昊杨,边后琴,王悉.基于改进RT-DETR的极端天气下交通标志检测方法[J].电子测量技术,2025,48(9):56-64

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  • 在线发布日期: 2025-05-23
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