Abstract
Underwater pipelines of oil and gas need periodic inspection to prevent damage due to the biological activity of water, turbulent current and tidal abrasion. Currently, vision-based autonomous underwater vehicle plays an important role in this field. A system has been designed to help an autonomous vehicle in sea-bottom survey operation. Image understanding and object recognition directly affect the accuracy of inspection. An image smoothing method based on mathematical morphology is proposed. The disturbances on acquired images caused by the motion are partially removed. A series of algorithms about image preprocessing, segmentation and recognition are proposed to access pipeline contours from the top-view images effectively. Navigation data based on Hough transformation is presented after the analysis of contours. Finally, the processing effect on a pipeline image demonstrates the effectiveness of the system.
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