Automated pipe defect detection and categorization using camera/laser-based profiler and artificial neural network
Automated pipe defect detection and categorization using camera/laser-based profiler and artificial neural network
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DOI：
10.1109/tase.2006.873225
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发表时间：
2007-01-01
影响因子：
5.6
通讯作者：
Senevatne, L. D.
中科院分区：
文献类型：
--
作者：
Duran, O.;Althoefer, K.;Senevatne, L. D.
闭路电视(CCTV)目前被用于许多检测应用中，例如对不可到达的管道表面的检测。这种以人为本的方法基于对原始图像的离线分析，由于要评估的数据量过大，主观性很强，容易出错。最近提出了激光剖面仪，以投射清晰的光图案，改善标准闭路电视系统的照明，并增强评估过程的自动化能力。这项研究表明，可以从获取的激光投影中提取与潜在缺陷相关的位置(几何)信息和强度信息。虽然大多数研究人员专注于分析从采集的轮廓仪信号中获得的位置信息，但这里也利用了反射光中包含的强度信息来进行缺陷分类和可视化。本文描述了管状结构缺陷自动分类的新策略，并探索了融合强度和位置信息的新方法，实现了改进的多变量缺陷分类。对所获取的照相机/激光图像进行处理，以便提取信号信息，以用于可视化和创建地图以供进一步评估。然后，采用基于图像处理和人工神经网络的两阶段分类方法对图像进行分类。首先用二进制分类器识别缺陷管段，然后在第二阶段将缺陷分类为孔洞、裂纹和突出障碍物等不同类型。提供了实验结果。给实践者的提示-本文提出的方法旨在自动检查不可接触的管道表面。这种方法被认为用于检查下水道；然而，它可以用于许多其他工业应用，也可以扩展到其他形状而不是管状结构。例如，由激光二极管和环形投影仪组成的激光环形轮廓仪可以容易地集成到现有的闭路电视系统中。提出的算法通过分析连续记录的包含反射光环的摄像机图像，识别缺陷区域并对缺陷类型进行分类。该算法可以在线使用，它利用了激光环反射后的变形及其强度的变化。使用人工智能算法将这两种数据结合在一起的事实使该方法足够稳健，可以在恶劣的环境中工作。
Closed-circuit television (CCTV) is currently used in many inspection applications, such as the inspection of nonaccessible pipe surfaces. This human-oriented approach based on offline analysis of the raw images is highly subjective and prone to error because of the exorbitant amount of data to be assessed. Laser profilers have been recently proposed to project well-defined light patterns, improving the illumination of standard CCTV systems as well as enhancing the capability of automating the assessment process. This research shows that positional (geometrical) as well as intensity information, related to potential defects, can be extracted from the acquired laser projections. While most researchers focus on the analysis of positional information obtained from the acquired profiler signals, here the intensity information contained within the reflected light is also exploited for the purpose of defect classification and visualization. This paper describes novel strategies created for the automation of defect classification in tubular structures and explores new methods to fuse intensity and positional information, achieving improved multivariable defect classification. The acquired camera/laser images are processed in order to extract signal information for the purpose of visualization and map creation for further assessment. Then, a two-stage approach based on image processing and artificial neural networks is used to classify the images. First, a binary classifier identifies defective pipe sections, and then in a second stage, the defects are classified into different types, such as holes, cracks, and protruding obstacles. Experimental results are provided.Note to Practitioners-The method presented in this paper aims to automate the inspection of nonaccessible pipe surfaces. The method was thought to be employed in the inspection of sewers; however, it could be used in many other industrial applications and could also be extended to other shapes rather than tubular structures. A laser ring profiler, consisting, for instance, of a laser diode and a ring projector, can be easily integrated into existing closed-circuit television systems. The proposed algorithm identifies defective areas and categorizes the types of defects, analyzing the successive recorded camera images that will contain the reflected ring of light. The algorithm, that can be used online, makes use of the deformation of the reflected laser ring together with its changes in intensity. The fact of combining the two kinds of data using artificial-intelligent algorithms makes the method robust enough to work in harsh environments.