Water Journal : Water Journal May 2012
refereed paper pipeline cleaning & maintenance water MAY 2012 59 Research Context Colour images provide significantly different information from greyscale images or range images (where the pixel values are distances). This can augment the effectiveness of some of the image processing tasks that are to be carried out on the images. However, in some other respects, the tasks are more difficult. With colour images, where the information available is that of colour and intensity, there may be no way of determining whether a defect or feature is an intrusion or an extrusion. This is because a patch of pipe surface looks essentially the same if it is translated in space. This means that it may be difficult to distinguish between categories such as "corrosion" and "deposit" for defects. On the other hand it may be that corrosions tend to have a particular colour or texture signature while deposits have another, in which case the colour image data would be sufficient to distinguish between them. With range images it can be determined whether a defect or feature is an intrusion or an extrusion by simply comparing the range values on the defect with the nearby range values off the defect. For this reason we may also assume, when discussing properties related to range values of defects, that the interpretation system has available an unwrapped pipe range image. This range image may be provided by the inspection system, or else possibly constructed by processing the colour images obtained by the inspection system using photogrammetry. If photogrammetry is used, then the cost of an inspection device would only be the cost of a colour image acquisition system such as in a commercially available system. The image matching operation for stereo photogrammetry is difficult for the images obtained from the inside of pipes. Research carried out with ANU (Zhang et al., 2011) has been directed to making this matching process possible under these conditions. Figure 3 shows the result of the 3D reconstruction of the surface of a pipe using these techniques. 3D pipe reconstruction is useful for detecting and quantifying deformation of pipes. In general, the system being developed by our research is similar to the PIRAT interpretation system. It consists of pre-processing, segmentation, classification, image analysis and high-level system modules. Pre-processing carries out such operations as smoothing and filtering to put the pipe image in a suitable form for the subsequent processing modules. The segmentation module partitions the input image into meaningful subsets. In the case of two-class segmentation, each subset is either a "region of interest" (ROI) or "good pipe". Segmentation can be effected by pixel labelling combined with connected component labelling. The image classification module classifies each ROI output by segmentation as being in one of a number of classes such as "hole", "corrosion", "pipe connection", "deposit" and "tree root". A schematic diagram for the proposed system is given in Figure 4. A decision tree classifier can be implemented. The fundamental decision that has to be made about an ROI is whether it is an intrusion or an extrusion. This can be made by comparing the average range value of on-defect pixels with the average range value of off-defect pixels that are in a neighbourhood of the boundary of the defect. The difference of these values in the case when the defect is determined to be an extrusion can be used to assign the defect to being either a hole or corrosion. Alternatively, by using appropriate fuzzy membership functions, the defect can be assigned fuzzy membership function values in the classes "hole" and "corrosion". Figure 4. Schematic diagram of proposed system. Figure 3. Reconstructed pipe surface together with its triangulation state.
Water Journal July 2012
Water Journal April 2012