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首页> 外文期刊>Advanced engineering informatics >Multi-class US traffic signs 3D recognition and localization via image-based point cloud model using color candidate extraction and texture-based recognition
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Multi-class US traffic signs 3D recognition and localization via image-based point cloud model using color candidate extraction and texture-based recognition

机译：通过使用颜色候选提取和基于纹理的识别的基于图像的点云模型，实现多类美国交通标志的3D识别和定位

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摘要

Continuous condition monitoring and inspection of traffic signs are essential to ensure that safety and performance criteria are met. The use of 3D point cloud modeling by the construction industry has been significantly increased in recent years especially for recording the as-is conditions of facilities. The high-precision and dense 3D point clouds generated by photogrammetry can facilitate the process of asset condition assessment. This paper presents an automated computer-vision based method that detects, classifies, and localizes traffic signs via street-level image-based 3D point cloud models. The proposed pipeline integrates 3D object detection algorithm. An improved Structure-from-Motion (SfM) procedure is developed to create a 3D point cloud of roadway assets from the street level imagery. In order to assist with accurate 3D recognition and localization by color and texture features extraction, an automated process of point cloud cleaning and noise removal is proposed. Using camera pose information from SfM, the points within the bounding box of detected traffic signs are then projected into the cleaned point cloud by using the triangulation method (linear and non-linear) and the 3D points corresponding to the traffic sign in question are labeled and visualized in 3D. The proposed framework is validated using real-life data, which represent the most common types of traffic signs. The robustness of the proposed pipeline is evaluated by analyzing the accuracy in detection of traffic signs as well as the accuracy in localization in 3D point cloud model. The results promise to better and more accurate visualize the location of the traffic signs with respect to other roadway assets in 3D environment.
机译：连续状态监视和交通标志检查对于确保满足安全和性能标准至关重要。近年来，建筑业对3D点云建模的使用已大大增加，尤其是在记录设施的现状时。摄影测量法生成的高精度且密集的3D点云可以促进资产状况评估的过程。本文提出了一种基于计算机视觉的自动化方法，该方法可通过基于街道级别的基于图像的3D点云模型来检测，分类和定位交通标志。拟议中的管道集成了3D对象检测算法。开发了一种改进的动态结构（SfM）程序，以从街道级图像创建道路资产的3D点云。为了通过颜色和纹理特征提取来帮助进行准确的3D识别和定位，提出了一种自动清除点云和去除噪音的过程。使用来自SfM的摄像机姿态信息，然后使用三角剖分方法（线性和非线性）将检测到的交通标志边界框内的点投影到清洁的点云中，并标记与该交通标志对应的3D点并以3D可视化。所提出的框架已通过使用真实数据进行了验证，这些数据代表了最常见的交通标志类型。通过分析交通标志检测的准确性以及3D点云模型中的定位准确性来评估所提议管道的鲁棒性。结果有望更好，更准确地可视化交通标志相对于3D环境中其他道路资产的位置。

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