Automated Detection and Fine Segmentation of Defects Signature in Pipelines using US Thickness Images.

Summary This contribution introduces a robust and content oriented detector of interest zones for defect localization in oil pipeline intelligent inspection achieving good perfomances toward complexity ratio. The method self-processes the multidimensional data collected by a pipeline inspection device equipped with many ultrasonic sensors (up to 512). It introduces a new content oriented usage of the EM algorithm, adapted to fit the very peculiar nature of the data to first isolate candidates zone, followed with a segmentation step to both get fine contours of defects and reject false alarms. Obtained performances in terms of specificity and sensibility show that the proposed approach is compatible with a routine utilization by specialists.