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Deconvolution Feature Fusion for traffic signs detection in 5G driven unmanned vehicle

机译：5G驱动无人驾驶车辆中交通标志检测的解压缩特征融合

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摘要

Real-time and accurate recognition of distant traffic signs in a wide visual range is one of the key technologies in 5G driven unmanned vehicles. Most earlier studies focused on improving accurately of short rang traffic signs detection in manned driving assistance system, while little attention has been paid to distant range traffic signs recognition in the unmanned vehicle. At the same time, it is difficult to detect long-distance traffic signs due to their small size. This paper is oriented to the 5G driven unmanned vehicle scene, proposes a novel framework of Deconvolution Feature Fusion based on the backbone of YOLOv3 (DFF-YOLOv3) to enhance the accuracy of distant traffic signs recognition in a wide visual range for unmanned driving. The proposed framework has combined the deep and shallow feature maps to form a fusion module with a wider range and higher accuracy of distant information in a fixed monocular camera. Specifically, the deep feature map is up-sampled by deconvolution and then merged with the shallow feature map. The convolution module is used to learn the feature, and then through the dimensionality reduction of the convolutional layer to form a deconvolution feature fusion module, which finally replaces the original prediction layer to detect the target. Experimental results are provided to validate the framework, which can improve the recognition accuracy of distant traffic signs without reducing the entire visual range in unmanned vehicles. The result shows that the mean accuracy prediction (mAP) of the proposed DFF-YOLOv3 on distant traffic signs is 74.8%, which is higher than other classic detection algorithms. (C) 2021 Elsevier B.V. All rights reserved.
机译：在广泛的视觉范围内的远程交通标志的实时和准确识别是5G驱动无人驾驶车辆中的关键技术之一。最早期的研究专注于准确改善短响的交通标志检测，载人驾驶辅助系统，虽然在无人驾驶车辆中对远处的范围交通标志识别较少的关注。同时，由于其体积小，难以检测长途交通标志。本文以5G驱动的无人驾驶车辆场景为导向，提出基于YOLOV3（DFF-YOLOV3）的骨干的解卷积特征融合的新框架，以提高远程交通标志识别的准确性，以便无人驾驶。所提出的框架组合了深层和浅浅特征映射，以形成具有更宽范围和更高的固定单眼相机中的远程信息的融合模块。具体而言，深度特征图是由解码器上采样的，然后与浅层特征图合并。卷积模块用于学习该特征，然后通过卷积层的维度减小来形成解构特征融合模块，最终替换原始预测层以检测目标。提供了实验结果来验证框架，这可以提高遥感交通标志的识别准确性，而不会减少无人驾驶车辆中的整个视觉范围。结果表明，所提出的DFF-YOLOV3对远处交通标志的平均精度预测（MAP）为74.8％，其高于其他经典检测算法。 （c）2021 elestvier b.v.保留所有权利。

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  • 来源
    《Physical Communication》 |2021年第8期|101375.1-101375.11|共11页
  • 作者

    Ma Xinshu; Li Xiaohuan; Tang Xin; Zhang Bingqi; Yao Rongbin; Lu Jun;

  • 作者单位

    Guilin Univ Elect Technol Sch Informat & Commun Guilin 541000 Peoples R China;

    Guilin Univ Elect Technol Sch Informat & Commun Guilin 541000 Peoples R China|Res Inst Comprehens Transportat Big Data Nanning 530000 Peoples R China;

    Inst Informat Technol GUET Guilin 541004 Peoples R China;

    China Transport Telecommun & Informat Ctr Beijing 100000 Peoples R China;

    Guilin Univ Elect Technol Sch Informat & Commun Guilin 541000 Peoples R China;

    Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China|Peng Cheng Lab Shenzhen 518055 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Traffic signs; Deconvolution feature fusion; Unmanned vehicle; 5G;

    机译：交通标志;去卷积特征融合;无人驾驶车辆;5克;

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