论文标题
工业对象的腐蚀检测：从多传感器系统到5D功能空间
Corrosion Detection for Industrial Objects: From Multi-Sensor System to 5D Feature Space
论文作者
论文摘要
腐蚀是一种经常出现在工业应用中使用的金属制成物体表面上的损害形式。这些损坏可能取决于用过的对象的目的。基于光学的测试系统提供了一种非接触式数据采集的形式，然后可以将获得的数据用于分析对象的表面。在工业图像处理领域，这称为表面检查。我们提供了一个由旋转表组成的测试设置，该旋转表将物体旋转360度，以及工业RGB摄像机和激光三角剖分传感器，以获取2D和3D数据作为我们的多传感器系统。这些传感器在要测试的对象时获取数据需要全面旋转。此外，还应用了数据增强来准备新的数据或增强已经获得的数据。为了评估激光三角传感器对腐蚀检测的影响，一个挑战是首先融合两个域的数据。在数据融合过程之后，可以利用5个不同的通道来创建5D特征空间。除了图像的红色，绿色和蓝色通道（1-3）之外，还合并了来自激光三角传感器的其他范围数据（4）。作为第五通道，所述传感器提供了其他强度数据（5）。使用多通道图像分类，5D特征空间将导致与3D特征空间相反的结果，仅由图像的RGB通道组成。
Corrosion is a form of damage that often appears on the surface of metal-made objects used in industrial applications. Those damages can be critical depending on the purpose of the used object. Optical-based testing systems provide a form of non-contact data acquisition, where the acquired data can then be used to analyse the surface of an object. In the field of industrial image processing, this is called surface inspection. We provide a testing setup consisting of a rotary table which rotates the object by 360 degrees, as well as industrial RGB cameras and laser triangulation sensors for the acquisition of 2D and 3D data as our multi-sensor system. These sensors acquire data while the object to be tested takes a full rotation. Further on, data augmentation is applied to prepare new data or enhance already acquired data. In order to evaluate the impact of a laser triangulation sensor for corrosion detection, one challenge is to at first fuse the data of both domains. After the data fusion process, 5 different channels can be utilized to create a 5D feature space. Besides the red, green and blue channels of the image (1-3), additional range data from the laser triangulation sensor is incorporated (4). As a fifth channel, said sensor provides additional intensity data (5). With a multi-channel image classification, a 5D feature space will lead to slightly superior results opposed to a 3D feature space, composed of only the RGB channels of the image.
