CLASSIFICATION OF POLE-LIKE OBJECTS USING POINT CLOUDS AND IMAGES CAPTURED BY MOBILE MAPPING SYSTEMS
CLASSIFICATION OF POLE-LIKE OBJECTS USING POINT CLOUDS AND IMAGES CAPTURED BY MOBILE MAPPING SYSTEMS
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DOI：
10.5194/isprs-archives-xlii-2-731-2018
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
Y. Mori;K. Kohira;H. Masuda
中科院分区：
文献类型：
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作者：
Y. Mori;K. Kohira;H. Masuda
抽象的。基于车辆的移动的测绘系统（MMS）对于捕获路边物体的3D形状和图像是有效的。MMS上的激光扫描仪和摄像头在驾驶过程中同步捕获点云和连续数字图像。在本文中，我们提出了一种方法，用于检测和分类杆状物体使用点云和使用MMS捕获的图像。在我们的方法中，杆状物体检测从点云，然后目标对象，这是物体附着到杆，被提取用于识别类型的杆状物体。为了将每个目标对象与图像相关联，将目标对象的点投影到图像上，并裁剪目标对象的图像。每个柱状物体被表示为一个特征向量，这是从点云和图像计算。通过点处理计算点云的特征值，并且使用卷积神经网络计算裁剪图像的特征值。点云和图像的特征值是统一的，它们被用作机器学习的输入。在实验中，我们使用三种方法分类杆状物体。第一种方法仅使用点云，第二种方法仅使用图像，第三种方法同时使用点云和图像。实验结果表明，第三种方法可以最准确地分类杆状物体。
Abstract. The vehicle-based mobile mapping system (MMS) is effective for capturing 3D shapes and images of roadside objects. The laser scanner and cameras on the MMS capture point-clouds and sequential digital images synchronously during driving. In this paper, we propose a method for detecting and classifying pole-like objects using both point-clouds and images captured using the MMS. In our method, pole-like objects are detected from point-clouds, and then target objects, which are objects attached to poles, are extracted for identifying the types of pole-like objects. For associating each target object with images, the points of the target object are projected onto images, and the image of the target object is cropped. Each pole-like object is represented as a feature vector, which are calculated from point-clouds and images. The feature values of a point-cloud are calculated by point processing, and the ones of the cropped image are calculated using a convolutional neural network. The feature values of point-clouds and images are unified, and they are used as the input to machine learning. In experiments, we classified pole-like objects using three methods. The first method used only point-clouds, the second used only images, and the third used both point-clouds and images. The experimental results showed that the third method could most accurately classify pole-like objects.