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首页> 外文期刊>ISPRS Journal of Photogrammetry and Remote Sensing >Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data
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Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data

机译：用于移动激光扫描数据中交通标志检测和识别的可视化短语和分层深度模型

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摘要

This paper presents a novel algorithm for detection and recognition of traffic signs in mobile laser scanning (MIS) data for intelligent transportation-related applications. The traffic sign detection task is accomplished based on 3-D point clouds by using bag-of-visual-phrases representations; whereas the recognition task is achieved based on 2-D images by using a Gaussian-Bernoulli deep Boltzmann machine-based hierarchical classifier. To exploit high-order feature encodings of feature regions, a deep Boltzmann machine-based feature encoder is constructed. For detecting traffic signs in 3-D point clouds, the proposed algorithm achieves an average recall, precision, quality, and F-score of 0.956, 0.946, 0.907, and 0.951, respectively, on the four selected MLS datasets. For on-image traffic sign recognition, a recognition accuracy of 97.54% is achieved by using the proposed hierarchical classifier. Comparative studies with the existing traffic sign detection and recognition methods demonstrate that our algorithm obtains promising, reliable, and high performance in both detecting traffic signs in 3-D point clouds and recognizing traffic signs on 2-D images. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
机译：本文提出了一种用于智能交通相关应用的检测和识别移动激光扫描（MIS）数据中交通标志的新颖算法。交通标志检测任务是通过使用可视化词组表示基于3-D点云来完成的；而识别任务是通过使用基于高斯-伯努利的深玻尔兹曼机器深度分层分类器基于二维图像来完成的。为了利用特征区域的高阶特征编码，构造了基于Boltzmann机器的深度特征编码器。为了检测3-D点云中的交通标志，该算法在四个选定的MLS数据集上分别实现了0.956、0.946、0.907和0.951的平均召回率，精度，质量和F分数。对于图像上的交通标志识别，使用所提出的分层分类器可实现97.54％的识别精度。与现有交通标志检测和识别方法的比较研究表明，我们的算法在检测3D点云中的交通标志和识别2D图像上的交通标志方面均获得了有希望的，可靠的和高性能。 （C）2016国际摄影测量与遥感学会（ISPRS）。由Elsevier B.V.发布。保留所有权利。

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